R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.

Preparation work

Review of the material at hand: - sumStats.df: all variables from GenTree fieldwork, averaged by population. NB plenty of missing data, together with

summary statistics derived from stairway plot analyses

See demoHistory8.Rmd for generation of values and df.

The way forward should include at least:

Start from data as recorded in ‘gtree_allspecies_env_dataset_extra_samples_20200908.csv’

creating a full and clean data set

bioMeanBy <- function(x,y) by(data = x, INDICES = y, FUN = mean, na.rm = T)
bioVarBy <- function(x,y) by(data = x, INDICES = y, FUN = var, na.rm = T)
library(data.table)
library(stats)
library(pspline)
library(corrr)
sourceEnvData.df <- read.csv(file = "gtree_allspecies_env_dataset_extra_samples_20200908.csv", header = T)
#these are individual-level data (mostly)
#obtaining the means:
envDataMeans.df <- data.frame(apply(X = sourceEnvData.df[c(6:9,11:99)], FUN = bioMeanBy, y = sourceEnvData.df[,5], MARGIN = 2))
#adding species names:
speciesInEnvDataMeans <- matrix(unlist(strsplit(row.names(envDataMeans.df), split = "_")),ncol = 3, byrow = T)[,2]
envDataMeans.df <-cbind(speciesInEnvDataMeans, envDataMeans.df)
names(envDataMeans.df)[1] <- "species"

an overview of variable distributions

#par(mfrow=c(3,3))
for (i in 2:93) hist(envDataMeans.df[,i], xlab=names(envDataMeans.df)[i], main="")

#for (i in 11:19) hist(envDataMeans.df[,i], xlab=names(envDataMeans.df)[i], main="")

##scaling the variables

#ad-hoc function to apply scale() on all data, by species
scaleBy <- function(x,y) by(data = x, INDICES = y, FUN = scale)
tmp <- apply(X = envDataMeans.df[2:94], FUN = scaleBy, y = envDataMeans.df[,1], MARGIN = 2)
## NOT working:
#envDataMeans.scaled.df <- data.frame(apply(X = envDataMeans.df[2:93], FUN = scaleBy, y = envDataMeans.df[,1], MARGIN = 2))

some checks are needed: is the order of populations within species maintained?

par(mfrow=c(2,2))
plot(x = subset(envDataMeans.df, species == "AA")$latwgs84, y = tmp$latwgs84$AA[,1])
plot(x = subset(envDataMeans.df, species == "PA")$bio11, y = tmp$bio11$PA[,1])
plot(x = subset(envDataMeans.df, species == "QP")$prec02, y = tmp$prec02$QP[,1])
plot(x = subset(envDataMeans.df, species == "PN")$tmax12, y = tmp$tmax12$PN[,1])

#everything seems to be fine
#but population names must be repositioned in front of values

reassembling a table

#attempt one
do.call(what = c, args = tmp$latwgs84) #oh, WOW.
##          AA1          AA2          AA3          AA4          AA5          AA6 
##  0.660171357  0.697835260  0.588803075  0.597080470  1.556240368  1.493407739 
##          AA7          AA8          AA9         AA10         AA11         AA12 
## -0.626350552 -0.638418636 -0.117448840 -0.866189443 -1.158301684 -0.632377955 
##         AA13          BP1          BP2          BP3          BP4          BP5 
## -1.554451159 -0.794158017 -0.818289200 -0.087964162  0.002611020 -1.352135646 
##          BP6          BP7          BP8          BP9         BP10         BP11 
## -1.261889468  1.773752891  1.169975198 -1.066526865 -0.984311275 -1.065563174 
##         BP12         BP13         BP14         BP15         BP16         BP17 
##  0.218796386  0.603111484 -1.141729195 -1.328315262  0.321350737  0.268544479 
##         BP18         BP19         BP20         BP21         BP22         BP23 
##  0.934166784  1.060550097 -0.131106538  0.178918592  0.472289480  1.567389931 
##         BP24          FS1          FS2          FS3          FS4          FS5 
##  1.460531724 -0.169332556 -0.153105166 -0.031350933 -0.034325228  0.069042641 
##          FS6          FS7          FS8          FS9         FS10         FS11 
##  0.067934702  0.700316055  0.694335694 -0.882002058 -0.874522106 -0.600280135 
##         FS12         FS13         FS14         FS15         FS16         FS17 
## -0.601379044 -1.033007337 -1.032516224  0.768066118  0.785488864 -1.276609404 
##         FS18         FS19         FS20         FS21         FS22         FS23 
## -1.278049953 -0.911476703 -0.913962592  2.197686310  2.233286637  1.592784022 
##         FS24         FS25         FS26         FS27         FS28           OB 
##  1.677757371 -0.202872637 -0.181126692 -0.305389823 -0.305389823          NaN 
##          PA1          PA2          PA3          PA4          PA5          PA6 
## -0.660853274 -0.660301720 -0.850016055 -0.850532441 -0.487523916 -0.487843523 
##          PA7          PA8          PA9         PA10         PA11         PA12 
##  1.735459380  1.123146040 -1.096875801 -1.456342026 -1.462008065 -0.817863913 
##         PA13         PA14         PA15         PA16         PA17         PA18 
## -0.821171367  0.137978376  0.317123128  0.887423939  1.097701435  1.693617100 
##         PA19         PA20         PA21         PA22         PA23         PA24 
## -0.003792946 -0.801309371  0.213014891  0.232129054  0.052234731  1.520354632 
##         PA25          PC1          PC2          PC3          PC4          PC5 
##  1.446251713  0.956096507  0.635036903  0.206737351  0.684857401 -0.025796084 
##          PC6          PC7          PC8          PC9         PC10          PH1 
##  1.190225067 -1.917749758 -1.282343327 -0.630616039  0.183551977 -1.729001888 
##          PH2          PH3          PH4          PH5          PH6          PH7 
##  0.690542909  0.738583953 -0.636895376 -0.195832516 -0.623561212  1.342337799 
##          PH8          PH9         PH10         PH11         PH12         PH13 
##  1.229133544 -1.355144216 -0.831271434 -0.187909922  0.959418450  0.599599907 
##          PN1          PN2          PN3          PN4          PN5          PN6 
##  1.559508463  1.560255038 -0.309592257 -0.079673711 -0.944857246 -0.948816909 
##          PN7          PN8          PN9         PN10         PN11         PN12 
##  0.545852140  0.156108156 -1.176075978 -0.622569294  1.157427269 -0.897565672 
##          PO1          PO2          PO3          PO4          PO5          PO6 
## -0.323857008  0.412005899  0.260625567  1.459161461  1.131590942  0.694608802 
##          PO7          PO8          PO9         PO10         PO11         PO12 
##  0.790790880 -1.246063238 -0.895474060  0.654436277  0.497051697 -0.116804423 
##         PO13         PO14         PO15         PO16         PO17         PO18 
## -0.130354870 -0.081381445 -0.535127969  1.003823076  1.547244964 -1.119410010 
##         PO19         PO20         PO21         PO22         PO23          PP1 
## -0.003145183  0.002871695 -0.558607154 -0.520086326 -2.923899571 -1.185990003 
##          PP2          PP3          PP4          PP5          PP6          PP7 
## -1.183854607 -0.212682053 -0.228728623  0.097999082  0.099465130 -0.306050336 
##          PP8          PP9         PP10         PP11         PP12         PP13 
## -0.307437102  1.132458523  1.078708482  0.638255274  0.633746917  0.217252295 
##         PP14         PP15         PP16         PP17         PP18         PP19 
##  0.234723108  0.595194426  0.871205102  0.860012349  0.609345214  0.611477135 
##         PP20         PP21         PP22         PP23         PP24         PP25 
##  0.975524164  1.013528708 -2.712622232 -2.038643336 -0.344200809 -1.148686808 
##          PS1          PS2          PS3          PS4          PS5          PS6 
## -0.707642005 -0.712297723  0.124861803  0.091191432 -1.303634631 -1.307321614 
##          PS7          PS8          PS9         PS10         PS11         PS12 
##  1.663129573  1.104747674 -0.773561829 -0.792781313  0.634709117  0.585443967 
##         PS13         PS14         PS15         PS16         PS17         PS18 
## -1.401168009 -1.392120280 -0.894087772 -0.893962679  0.213801175  0.386912859 
##         PS19         PS20         PS21         PS22         PS23          QP1 
##  0.895975838  1.081569641  1.011239018  1.010935460  1.374060297 -0.270227378 
##          QP2          QP3          QP4          QP5          QP6          QP7 
## -0.270162866  0.656147022  0.422645875 -1.135120948 -1.134960367 -0.413033126 
##          QP8          QP9         QP10         QP11         QP12         QP13 
## -0.406099393  0.732430620  0.989767379 -0.901891398 -0.917802466 -1.683799416 
##         QP14         QP15         QP16         QP17         QP18         QP19 
## -1.683394775  0.732428621  1.359671718  1.323005134  0.605932017  0.986667717 
##         QP20          TB1          TB2          TB3          TB4          TB5 
##  1.007796029  0.004458195  0.085347043 -0.669433388 -0.665950293 -0.574473344 
##          TB6          TB7          TB8          TB9         TB10         TB11 
## -0.774273977  1.012821061 -1.045168945 -0.693976538  1.831202897  1.489447290
# nesting do.call() within lapply(): 
tmp2 <- lapply(X = tmp, FUN = function(x) do.call(what = c, args = x))
# using do.call to fold the list into data.frame:
envDataMeans.scaled.df <- data.frame(do.call(what = cbind, args = tmp2))
#re-inserting species names in the table
speciesInEnvDataMeansScaled <- unlist(by(data = envDataMeans.df, 
                                         INDICES = envDataMeans.df$species, FUN = function(x) x$species))
envDataMeans.scaled.df <- cbind(speciesInEnvDataMeansScaled, envDataMeans.scaled.df)
names(envDataMeans.scaled.df)[1] <- "species"
#getting reordered population names from envDataMeans.df
row.names(envDataMeans.scaled.df) <- unlist(by(data = envDataMeans.df, INDICES = envDataMeans.df$species, FUN = row.names))

scaling and centering diversity statistics:

1. stairway plot historical demography statistics:

data.dt <- fread("all_results_stairwayplot.tsv", header = T, nrows = 1231558)

Computing smoothed curve and derivatives As a guidance, I used https://stackoverflow.com/questions/43615469/how-to-calculate-the-slope-of-a-smoothed-curve-in-r

#using birch and CH pop 5 as test data set
tmp.dt <- subset(data.dt, species == "Bpendula" & pop == "CH_BP_05")
#there are duplicated x values, keep only one of each
tmp.dt <- tmp.dt[ which(!duplicated(tmp.dt$year)),]
tmp_smoothed.curve <- smooth.Pspline(x = tmp.dt$year,
                                     y = tmp.dt$Ne_median, 
                                     df = 20,
                                     spar = 20,
                                     method = 2)
tmp.f1 <- predict(tmp_smoothed.curve, x = tmp.dt$year, nderiv=1)
tmp.f2 <- predict(tmp_smoothed.curve, x = tmp.dt$year, nderiv=2)

building a generalised function

derivatives <- function(x)
{
  tmp.dt <- x[which(!duplicated(x$year)),]
  temp_var <- tryCatch(smooth.Pspline(x = tmp.dt$year,
                                     y = tmp.dt$Ne_median, 
                                     df = 20,
                                     spar = 20,
                                     method = 2),
                       error=function(e) e)
  ifelse(inherits(temp_var, "error"),
         {
           sumstats <- rep(NA, 6)
         },
         {
           tmp_smoothed.curve <- smooth.Pspline(x = tmp.dt$year,
                                     y = tmp.dt$theta_per_site_median, 
                                     df = 20,
                                     spar = 20,
                                     method = 2)
  tmp.f1 <- predict(tmp_smoothed.curve, x = tmp.dt$year, nderiv=1)
  tmp.f2 <- predict(tmp_smoothed.curve, x = tmp.dt$year, nderiv=2)
  sumstats <- c(mean(tmp.f1), median(tmp.f1), sd(tmp.f1),mean(tmp.f2), median(tmp.f2), sd(tmp.f2))
         })
  names(sumstats) <- c("mean_f1", "median_f1", "sd_f1", "mean_f2", "median_f2", "sd_f2")
  return(sumstats)
}

application

derivatives.sumstats <- by(data = data.dt, INDICES = data.dt$pop, FUN = derivatives, simplify = F)
derivatives.sumstats.df <- data.frame(do.call(what = rbind, args = derivatives.sumstats))
write.table(file = "derivatives_sumstats.txt", x = derivatives.sumstats.df, sep = "\t", quote = F)

adding mean, median, and sd of theta

theta_means <- by(data = data.dt, INDICES = data.dt$pop, FUN = function(x) mean(x$theta_per_site_median), simplify = F)
theta_medians <- by(data = data.dt, INDICES = data.dt$pop, FUN = function(x) median(x$theta_per_site_median), simplify = F)
theta_sd <- by(data = data.dt, INDICES = data.dt$pop, FUN = function(x) sd(x$theta_per_site_median), simplify = F)
theta_stats.df <- do.call(what = data.frame, args = list(unlist(theta_means), unlist(theta_medians), unlist(theta_sd)))
names(theta_stats.df) <- c("mean_medianTheta", "median_medianTheta", "sd_medianTheta")
all.sumstats.df <- merge(by = 0, theta_stats.df, derivatives.sumstats.df)
row.names(all.sumstats.df) <- all.sumstats.df[,1]
all.sumstats.df <- all.sumstats.df[,-1]
#adding species names:
speciesInTheta <- matrix(unlist(strsplit(row.names(all.sumstats.df), split = "_")),ncol = 3, byrow = T)[,2]
all.sumstats.df <-cbind(speciesInTheta, all.sumstats.df)
names(all.sumstats.df)[1] <- "species"

Actual scaling and centering

tmp.theta <- apply(X = all.sumstats.df[2:10], FUN = scaleBy, y = all.sumstats.df[,1], MARGIN = 2)

reassembling a table

#attempt one
# do.call(what = c, args = tmp$latwgs84) #oh, WOW.
# nesting do.call() within lapply(): 
tmp2.theta <- lapply(X = tmp.theta, FUN = function(x) do.call(what = c, args = x))
# using do.call to fold the list into data.frame:
all.sumstats.scaled.df <- data.frame(do.call(what = cbind, args = tmp2.theta))
#getting reordered population names from envDataMeans.df
row.names(all.sumstats.scaled.df) <- unlist(by(data = all.sumstats.df, INDICES = all.sumstats.df$species, FUN = row.names))

2. pi values

Importing pi stats

piStats.df <- read.table("piStats.csv", header = T)
row.names(piStats.df) <- piStats.df[,1]

Actual scaling and centering

tmp.pi <- apply(X = piStats.df[3:7], FUN = scaleBy, y = piStats.df[,2], MARGIN = 2)

reassembling a table

#attempt one
# do.call(what = c, args = tmp$latwgs84) #oh, WOW.
# nesting do.call() within lapply(): 
tmp2.pi <- lapply(X = tmp.pi, FUN = function(x) do.call(what = c, args = x))
# using do.call to fold the list into data.frame:
piStats.scaled.df <- data.frame(do.call(what = cbind, args = tmp2.pi))
#getting reordered population names from envDataMeans.df
row.names(piStats.scaled.df) <- unlist(by(data = piStats.df, INDICES = piStats.df$species, FUN = row.names))

3. Importing clustering info, scaling, centering

NB for the time being, I focus on conStruct results with K = 2 to keep it simple NB (2) data are on External drive for the time being, so path may not be permanent

#BP
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/BP/k2/k2_conStruct.results.Robj")
BP_admixtureValues.df <- data.frame(conStruct.results$chain_1$MAP$admix.proportions)
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/BP/k2/k2_data.block.Robj")
row.names(BP_admixtureValues.df) <- row.names(data.block$obsCov)
#FS
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/FS/k2/k2_conStruct.results.Robj")
FS_admixtureValues.df <- data.frame(conStruct.results$chain_1$MAP$admix.proportions)
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/FS/k2/k2_data.block.Robj")
row.names(FS_admixtureValues.df) <- row.names(data.block$obsCov)
#PA
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PA/k2/k2_conStruct.results.Robj")
PA_admixtureValues.df <- data.frame(conStruct.results$chain_1$MAP$admix.proportions)
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PA/k2/k2_data.block.Robj")
row.names(PA_admixtureValues.df) <- row.names(data.block$obsCov)
#PO
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PO/k2/k2_conStruct.results.Robj")
PO_admixtureValues.df <- data.frame(conStruct.results$chain_1$MAP$admix.proportions)
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PO/k2/k2_data.block.Robj")
row.names(PO_admixtureValues.df) <- row.names(data.block$obsCov)
#PP
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PP/k2/k2_conStruct.results.Robj")
PP_admixtureValues.df <- data.frame(conStruct.results$chain_1$MAP$admix.proportions)
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PP/k2/k2_data.block.Robj")
row.names(PP_admixtureValues.df) <- row.names(data.block$obsCov)
#PS
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PS/k2/k2_conStruct.results.Robj")
PS_admixtureValues.df <- data.frame(conStruct.results$chain_1$MAP$admix.proportions)
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/PS/k2/k2_data.block.Robj")
row.names(PS_admixtureValues.df) <- row.names(data.block$obsCov)
#QP
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/QP/k2/k2_conStruct.results.Robj")
QP_admixtureValues.df <- data.frame(conStruct.results$chain_1$MAP$admix.proportions)
load("/Volumes/temporaryStorage/Documents/projects/GenTree/GenTree_T4.4/construct/QP/k2/k2_data.block.Robj")
row.names(QP_admixtureValues.df) <- row.names(data.block$obsCov)
AllSp_admixtureValues.df <- do.call(what = rbind, args = list(BP_admixtureValues.df,
                                                              FS_admixtureValues.df,
                                                              PA_admixtureValues.df,
                                                              PO_admixtureValues.df,
                                                              PP_admixtureValues.df,
                                                              PS_admixtureValues.df,
                                                              QP_admixtureValues.df))
AllSp_admixtureValues.df$PopID <- row.names(AllSp_admixtureValues.df)
#adding species names
speciesInAdmix <- matrix(unlist(strsplit(row.names(AllSp_admixtureValues.df), split = "_")),ncol = 3, byrow = T)[,2]
AllSp_admixtureValues.df <-cbind(speciesInAdmix, AllSp_admixtureValues.df)
names(AllSp_admixtureValues.df)[1] <- "species"
## NOT USED
#merging admixture info into data frames:
# View(x = merge(AllSp_admixtureValues.df, bioClimPiClean.df[,-1], all.y = T))
# bioClimPiClean.df <- merge(AllSp_admixtureValues.df, bioClimPiClean.df[,-1], all.y = T)[-c(87,88,142),]
# names(bioClimPiClean.df)[2:3] <- c("admixLayer1", "admixLayer2")

Actual scaling and centering

tmp.admix <- apply(X = AllSp_admixtureValues.df[2:3], FUN = scaleBy, y = AllSp_admixtureValues.df[,1], MARGIN = 2)

reassembling a table

#attempt one
# do.call(what = c, args = tmp$latwgs84) #oh, WOW.
# nesting do.call() within lapply(): 
tmp2.admix <- lapply(X = tmp.admix, FUN = function(x) do.call(what = c, args = x))
# using do.call to fold the list into data.frame:
AllSp_admixtureValues.scaled.df <- data.frame(do.call(what = cbind, args = tmp2.admix))
#getting reordered population names from AllSp_admixtureValues.df
row.names(AllSp_admixtureValues.scaled.df) <- unlist(by(data = AllSp_admixtureValues.df, INDICES = AllSp_admixtureValues.df$species, FUN = row.names))

merging everything in a single table FOR WP4 SPECIES ONLY

allDivEnv.df <- merge(x = envDataMeans.scaled.df, y = AllSp_admixtureValues.scaled.df, all.y = T, by = 0)
row.names(allDivEnv.df) <- allDivEnv.df$Row.names
allDivEnv.df <- allDivEnv.df[-1]
names(allDivEnv.df)[95:96] <- c("AdmixLayer1", "AdmixLayer2")
allDivEnv.df <- merge(x = allDivEnv.df, y = piStats.scaled.df, by = 0)
row.names(allDivEnv.df) <- allDivEnv.df$Row.names
allDivEnv.df <- allDivEnv.df[-1]
allDivEnv.df <- merge(x = allDivEnv.df, y = all.sumstats.scaled.df, by = 0)
row.names(allDivEnv.df) <- allDivEnv.df$Row.names
names(allDivEnv.df)[1] <- "popId"

Inspection of correlations between dependent variables (i.e., gen div statistics): this will be useful to avoid reporting redundant information

#performed on normalised variables
#allDivEnv.df[is.nan(allDivEnv.df)==T] <- NA
pdf(file = "normalisedDivStatsPairwisePlots.pdf", width = 16.5, height = 11.7)
plot(allDivEnv.df[98:111], pch = ".", col = "blue")
dev.off()
## quartz_off_screen 
##                 2
divStats.cor <- round(cor(allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),98:111]),2)
corTest <- function(x,y)
{
  tmp <- list()
  for (i in 1:ncol(y)) tmp[[i]] <- cor.test(x,y[,i], alternative = "t", method = "p")
  names(tmp) <- colnames(y)
  return(tmp)
}
divStats.cortest <- apply(X = allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),98:111], MARGIN = 2, FUN = corTest,
      y = allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),98:111])
#correlate(x = allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),98:111])
# test <- corTest(x = allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),98],
#         y = allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),98:111])
# cor.test(allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),98], 
#          allDivEnv.df[-c(146, which(is.na(allDivEnv.df$mean_f1))),99])
# 
# 
# extracting p-values
divStats.cortest_pvals <- lapply(X = divStats.cortest, FUN = function(x) lapply(X = x, function(x) x$p.value))
# identifying significant correlations with bonferroni correction
#building a matrix of significance
divStats.cortest_signif <- matrix((unlist(divStats.cortest_pvals)) < 0.05 / 14^2, ncol = 14, byrow = T)
row.names(divStats.cortest_signif) <- row.names(divStats.cor)
colnames(divStats.cortest_signif) <- colnames(divStats.cor)

#which((unlist(divStats.cortest_pvals)) < 0.05 / 14^2)
#keeping only significant values:
divStats.cortest_signif_cor <- divStats.cor * divStats.cortest_signif
divStats.cortest_signif_cor[divStats.cortest_signif_cor == 0] <- NA
write.table(x = divStats.cortest_signif_cor, file = "divStats_correlations.txt")

Part 1. Variable selection

Linear Models for the 1st GenTree WP4 paper: including only Latitude, Longitude, and Admixture (for Layer 1)

NB the single row for OB is removed

library(glmulti)
## Loading required package: rJava
## Loading required package: leaps
# step_piAll <- function(x)
# {
#   #cat("\n********************************\n",unique(x$species),"\n")
#   # init_mod <- lm(pi_all ~ ., data = x)
#   # step(init_mod, scope = . ~ .^2, direction = 'forward', k = log(nrow(x)))
#   glmulti(pi_all ~ latwgs84 + lonwgs84 + AdmixLayer1, data = x, 
#           level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
#   # step(glm(formula = pi_all ~ 1, data = x),
#   #                    scope = ~ (latwgs84 + lonwgs84 + AdmixLayer1)^2,
#   #                    direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_piAll_T4_4", append = T)
# piAll_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_piAll)
# sink()
# #testing
# test <- glmulti(pi_all ~ latwgs84 + lonwgs84 + AdmixLayer1, data = subset(allDivEnv.df, species = "BP"), 
#           level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
piAll_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  piAll_by_species_T4_4.glm[[i]] <- 
  glmulti(pi_all ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_all~1+lonwgs84
## Crit= 64.6860050761268
## Mean crit= 70.0418366917362
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_all~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= -23.4581933274058
## Mean crit= 21.9413641543611
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_all~1+latwgs84+lonwgs84+lonwgs84:latwgs84+AdmixLayer1:lonwgs84
## Crit= 14.7683049005242
## Mean crit= 45.9578925371911
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_all~1+AdmixLayer1+lonwgs84:latwgs84+AdmixLayer1:latwgs84
## Crit= 63.0367645879122
## Mean crit= 69.0658978250577
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_all~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 41.853819703924
## Mean crit= 65.5948030215972
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_all~1+latwgs84+AdmixLayer1:latwgs84
## Crit= 59.5142191286362
## Mean crit= 67.4462304395737
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_all~1+latwgs84
## Crit= 54.413009595149
## Mean crit= 61.5923297473622
## Completed.
names(piAll_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_pi_0fold <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = pi_0fold ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_pi_0fold_T4_4", append = T)
# pi_0fold_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_pi_0fold)
# sink()
pi_0fold_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  pi_0fold_by_species_T4_4.glm[[i]] <- 
  glmulti(pi_0fold ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_0fold~1+lonwgs84
## Crit= 62.5270813109668
## Mean crit= 67.9762816233622
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_0fold~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= -20.0928655921797
## Mean crit= 17.5410545700456
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_0fold~1+latwgs84+lonwgs84+lonwgs84:latwgs84+AdmixLayer1:lonwgs84
## Crit= 18.8767969348413
## Mean crit= 49.8732904935239
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_0fold~1+AdmixLayer1+lonwgs84:latwgs84+AdmixLayer1:latwgs84
## Crit= 58.9901170988622
## Mean crit= 67.5515437486915
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_0fold~1+latwgs84+AdmixLayer1+lonwgs84:latwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 36.6167147347159
## Mean crit= 62.323815943163
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_0fold~1+lonwgs84+lonwgs84:latwgs84+AdmixLayer1:latwgs84
## Crit= 68.6747158993472
## Mean crit= 74.433031951216
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_0fold~1+latwgs84
## Crit= 53.9514921316646
## Mean crit= 61.2291210946449
## Completed.
names(pi_0fold_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_pi_4fold <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = pi_4fold ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_pi_4fold_T4_4", append = T)
# pi_4fold_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_pi_4fold)
# sink()
pi_4fold_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  pi_4fold_by_species_T4_4.glm[[i]] <- 
  glmulti(pi_4fold ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_4fold~1+lonwgs84
## Crit= 66.0308710003975
## Mean crit= 70.9449711458194
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_4fold~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= -12.8323519264042
## Mean crit= 24.592366293605
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_4fold~1+latwgs84+lonwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 24.0677479220453
## Mean crit= 51.9206289323742
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_4fold~1+AdmixLayer1+AdmixLayer1:latwgs84
## Crit= 63.1380445344821
## Mean crit= 69.3053735988875
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_4fold~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 48.6050061834465
## Mean crit= 67.9503592097117
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_4fold~1+lonwgs84+AdmixLayer1
## Crit= 68.8078518796993
## Mean crit= 74.3196311639594
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_4fold~1+latwgs84
## Crit= 54.7164677922325
## Mean crit= 61.9401379183155
## Completed.
names(pi_4fold_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_pi0_pi4 <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = pi0_pi4 ~ 1, data = x),
#                      scope = ~  latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_pi0_pi4_T4_4", append = T)
# pi0_pi4_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_pi0_pi4)
# sink()
pi0_pi4_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  pi0_pi4_by_species_T4_4.glm[[i]] <- 
  glmulti(pi0_pi4 ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi0_pi4~1+lonwgs84
## Crit= 60.5474449890675
## Mean crit= 67.946611629445
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi0_pi4~1+lonwgs84+AdmixLayer1:latwgs84
## Crit= 41.7861376578854
## Mean crit= 49.1968348443163
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi0_pi4~1
## Crit= 71.5081058172764
## Mean crit= 75.1341442106822
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi0_pi4~1+AdmixLayer1+lonwgs84:latwgs84+AdmixLayer1:lonwgs84
## Crit= 26.4602338549257
## Mean crit= 50.1540257656888
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi0_pi4~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84
## Crit= 60.5627008020203
## Mean crit= 68.8911192597046
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi0_pi4~1+latwgs84
## Crit= 68.4407798698793
## Mean crit= 75.3346170671779
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi0_pi4~1+latwgs84
## Crit= 53.3538711162489
## Mean crit= 58.6687120359456
## Completed.
names(pi0_pi4_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_pi_putNeu <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = pi_putNeu ~ 1, data = x),
#                      scope = ~  latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_pi_putNeu_T4_4", append = T)
# pi_putNeu_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_pi_putNeu)
# sink()
pi_putNeu_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  pi_putNeu_by_species_T4_4.glm[[i]] <- 
  glmulti(pi_putNeu ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_putNeu~1+lonwgs84
## Crit= 64.9916673626204
## Mean crit= 70.3876099826843
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_putNeu~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= -22.2710812480432
## Mean crit= 23.136090864477
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_putNeu~1+latwgs84+lonwgs84+lonwgs84:latwgs84+AdmixLayer1:lonwgs84
## Crit= 14.9340119161957
## Mean crit= 44.8893078248208
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_putNeu~1+AdmixLayer1+AdmixLayer1:latwgs84
## Crit= 63.7483895760611
## Mean crit= 69.5367257152127
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_putNeu~1+latwgs84+AdmixLayer1+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 45.0810875045569
## Mean crit= 67.1625220816438
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_putNeu~1+AdmixLayer1+lonwgs84:latwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 58.5212487339944
## Mean crit= 65.466846224388
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: pi_putNeu~1+latwgs84
## Crit= 54.3896209662842
## Mean crit= 61.5930032948175
## Completed.
names(pi_putNeu_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_mean_f1 <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = mean_f1 ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_mean_f1(theta)_T4_4", append = T)
# mean_f1_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_mean_f1)
# sink()
mean_f1_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  mean_f1_by_species_T4_4.glm[[i]] <- 
  glmulti(mean_f1 ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f1~1
## Crit= 61.7231399875439
## Mean crit= 68.2911056128203
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f1~1+latwgs84
## Crit= 69.132052492742
## Mean crit= 75.2755161833566
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f1~1
## Crit= 70.5197704201441
## Mean crit= 78.527063606074
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f1~1+latwgs84+lonwgs84
## Crit= 48.1789687229579
## Mean crit= 55.1525997186282
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f1~1+latwgs84+AdmixLayer1
## Crit= 51.3432509387448
## Mean crit= 55.518820089849
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f1~1+latwgs84
## Crit= 65.5429411149059
## Mean crit= 73.0722059642032
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f1~1
## Crit= 49.918594168828
## Mean crit= 55.5847865824735
## Completed.
names(mean_f1_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_median_f1 <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = median_f1 ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_median_f1(theta)_T4_4", append = T)
# median_f1_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_median_f1)
# sink()
median_f1_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  median_f1_by_species_T4_4.glm[[i]] <- 
  glmulti(median_f1 ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f1~1
## Crit= 61.7231399875439
## Mean crit= 68.6835304760217
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f1~1
## Crit= 73.4437265084691
## Mean crit= 79.9497816772005
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f1~1
## Crit= 70.5197704201441
## Mean crit= 78.05420502209
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f1~1+latwgs84+lonwgs84+AdmixLayer1:lonwgs84
## Crit= 47.5722618638525
## Mean crit= 54.2078493802
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f1~1+latwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 50.6538477263543
## Mean crit= 55.7234998583543
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f1~1+latwgs84
## Crit= 68.7636392421649
## Mean crit= 74.2716548010598
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f1~1+AdmixLayer1+AdmixLayer1:latwgs84
## Crit= 48.7274677120161
## Mean crit= 52.1582000861066
## Completed.
names(median_f1_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_sd_f1 <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = sd_f1 ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_sd_f1(theta)_T4_4", append = T)
# sd_f1_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_sd_f1)
# sink()
sd_f1_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  sd_f1_by_species_T4_4.glm[[i]] <- 
  glmulti(sd_f1 ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f1~1+latwgs84
## Crit= 61.6799081802282
## Mean crit= 67.5839064384881
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f1~1
## Crit= 73.4437265084691
## Mean crit= 80.5986191095661
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f1~1
## Crit= 70.5197704201441
## Mean crit= 75.270265993595
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f1~1+lonwgs84+AdmixLayer1:latwgs84
## Crit= 46.0975465416833
## Mean crit= 53.8577956190487
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f1~1+AdmixLayer1
## Crit= 49.8708841210917
## Mean crit= 55.8881003751422
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f1~1
## Crit= 70.5197704201441
## Mean crit= 77.3042717070105
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f1~1
## Crit= 49.918594168828
## Mean crit= 55.4554257232742
## Completed.
names(sd_f1_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_mean_f2 <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = mean_f2 ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_mean_f2(theta)_T4_4", append = T)
# mean_f2_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_mean_f2)
# sink()
mean_f2_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  mean_f2_by_species_T4_4.glm[[i]] <- 
  glmulti(mean_f2 ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f2~1
## Crit= 61.7231399875439
## Mean crit= 67.9495235119085
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f2~1+AdmixLayer1:lonwgs84
## Crit= 66.126612759353
## Mean crit= 73.122344957574
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f2~1+AdmixLayer1:latwgs84
## Crit= 70.3165447778428
## Mean crit= 74.2267179846146
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f2~1
## Crit= 52.8797182461919
## Mean crit= 59.9984033355274
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f2~1+lonwgs84
## Crit= 50.3403368199975
## Mean crit= 54.8942311926022
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f2~1+latwgs84
## Crit= 69.829666358275
## Mean crit= 75.3604761534615
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_f2~1+latwgs84+lonwgs84:latwgs84+AdmixLayer1:lonwgs84
## Crit= 46.172786800883
## Mean crit= 53.4526655301883
## Completed.
names(mean_f2_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_median_f2 <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = median_f2 ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_median_f2(theta)_T4_4", append = T)
# median_f2_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_median_f2)
# sink()
median_f2_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  median_f2_by_species_T4_4.glm[[i]] <- 
  glmulti(median_f2 ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f2~1
## Crit= 61.7231399875439
## Mean crit= 67.9773391067349
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f2~1+latwgs84+AdmixLayer1+AdmixLayer1:lonwgs84
## Crit= 69.4541897816253
## Mean crit= 75.1662137109424
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f2~1+lonwgs84+AdmixLayer1
## Crit= 69.9400919849175
## Mean crit= 74.8885011030612
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f2~1+lonwgs84+AdmixLayer1:latwgs84
## Crit= 41.7262707354204
## Mean crit= 47.1530386021784
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f2~1+latwgs84+AdmixLayer1
## Crit= 46.4784780304148
## Mean crit= 53.4392389637639
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f2~1
## Crit= 70.5197704201441
## Mean crit= 77.7899326801508
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_f2~1+AdmixLayer1+AdmixLayer1:latwgs84
## Crit= 44.0028599589757
## Mean crit= 49.5850388071119
## Completed.
names(median_f2_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_sd_f2 <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = sd_f2 ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_sd_f2(theta)_T4_4", append = T)
# sd_f2_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_sd_f2)
# sink()
sd_f2_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  sd_f2_by_species_T4_4.glm[[i]] <- 
  glmulti(sd_f2 ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f2~1
## Crit= 61.7231399875439
## Mean crit= 67.9463832692104
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f2~1+AdmixLayer1:lonwgs84
## Crit= 66.4301940826138
## Mean crit= 73.1306722650337
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f2~1
## Crit= 70.5197704201441
## Mean crit= 78.411378567466
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f2~1
## Crit= 52.8797182461919
## Mean crit= 59.8668280954871
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f2~1+latwgs84+AdmixLayer1
## Crit= 46.6686717559761
## Mean crit= 53.4719546903156
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f2~1
## Crit= 70.5197704201441
## Mean crit= 76.3381396549914
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_f2~1+latwgs84+lonwgs84:latwgs84+AdmixLayer1:lonwgs84
## Crit= 49.3807603056406
## Mean crit= 54.1118271211957
## Completed.
names(sd_f2_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_mean_medianTheta <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = mean_medianTheta ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_mean_median(theta)_T4_4", append = T)
# mean_medianTheta_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_mean_medianTheta)
# sink()
mean_medianTheta_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  mean_medianTheta_by_species_T4_4.glm[[i]] <- 
  glmulti(mean_medianTheta ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_medianTheta~1+latwgs84
## Crit= 62.8048858208063
## Mean crit= 72.3118501935249
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_medianTheta~1+latwgs84+lonwgs84+lonwgs84:latwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 6.93857334333576
## Mean crit= 62.5926520041192
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_medianTheta~1+lonwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 64.8882541155803
## Mean crit= 76.2364675380037
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_medianTheta~1
## Crit= 67.5919400237546
## Mean crit= 73.1362158762125
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_medianTheta~1+AdmixLayer1+lonwgs84:latwgs84
## Crit= 67.499988955101
## Mean crit= 76.0210168713299
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_medianTheta~1+latwgs84+lonwgs84+AdmixLayer1:latwgs84
## Crit= 51.2770601448082
## Mean crit= 64.4133636353182
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: mean_medianTheta~1
## Crit= 58.7812650159752
## Mean crit= 65.6417923118325
## Completed.
names(mean_medianTheta_by_species_T4_4.glm) <- names(bySpecies.ls)
# step_median_medianTheta <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = median_medianTheta ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_median_median(theta)_T4_4", append = T)
# median_medianTheta_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_median_medianTheta)
# sink()
median_medianTheta_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  median_medianTheta_by_species_T4_4.glm[[i]] <- 
  glmulti(median_medianTheta ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_medianTheta~1+latwgs84
## Crit= 62.1118092578636
## Mean crit= 72.0551287354967
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_medianTheta~1+latwgs84+AdmixLayer1+AdmixLayer1:lonwgs84
## Crit= 38.8906484017752
## Mean crit= 66.0333038350318
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_medianTheta~1+lonwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 70.7459398727705
## Mean crit= 77.8678108922215
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_medianTheta~1
## Crit= 67.5919400237546
## Mean crit= 73.3977179380594
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_medianTheta~1+lonwgs84
## Crit= 64.5340495752154
## Mean crit= 72.7459093604912
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_medianTheta~1+lonwgs84
## Crit= 68.2451187691417
## Mean crit= 73.4488360662963
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: median_medianTheta~1
## Crit= 58.7812650159752
## Mean crit= 65.6585735442999
## Completed.
names(median_medianTheta_by_species_T4_4.glm) <- names(bySpecies.ls)
##NOT USED:
# step_sd_medianTheta <- function(x)
# {
#   cat("\n********************************\n",unique(x$species),"\n")
#   step(glm(formula = sd_medianTheta ~ 1, data = x),
#                      scope = ~ latwgs84 + lonwgs84 + AdmixLayer1,
#                      direction = "forward", k = log(nrow(x)))
# }
# sink(file = "glm_sd_median(theta)_T4_4", append = T)
# sd_medianTheta_by_species_T4_4.glm <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = step_sd_medianTheta)
# sink()
sd_medianTheta_by_species_T4_4.glm <- list()
bySpecies.ls <- by(allDivEnv.df[-146,], INDICES = factor(allDivEnv.df[-146,]$species), FUN = function(x) x)
for (i in 1:7) 
  {
  sd_medianTheta_by_species_T4_4.glm[[i]] <- 
  glmulti(sd_medianTheta ~ latwgs84 + lonwgs84 + AdmixLayer1, data = bySpecies.ls[[i]], 
          level = 2, method = "h",fitfunction = lm, crit = 'bic', plotty = F)
  }
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_medianTheta~1
## Crit= 70.5197704201441
## Mean crit= 76.2743715643472
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_medianTheta~1+AdmixLayer1+lonwgs84:latwgs84
## Crit= 67.3355497472131
## Mean crit= 77.045351975393
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_medianTheta~1+latwgs84+lonwgs84+AdmixLayer1:latwgs84+AdmixLayer1:lonwgs84
## Crit= 66.1173491775956
## Mean crit= 75.9172913637316
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_medianTheta~1
## Crit= 67.5919400237546
## Mean crit= 73.0442989157055
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_medianTheta~1+latwgs84+lonwgs84:latwgs84
## Crit= 74.9629716485368
## Mean crit= 80.2871376954467
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_medianTheta~1+latwgs84+lonwgs84+AdmixLayer1:latwgs84
## Crit= 54.2257643752469
## Mean crit= 64.5542011517097
## Completed.
## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
## 
## After 50 models:
## Best model: sd_medianTheta~1
## Crit= 58.7812650159752
## Mean crit= 65.400421015353
## Completed.
names(sd_medianTheta_by_species_T4_4.glm) <- names(bySpecies.ls)

Part 2. Estimation of “effects”

Estimation of slopes. Notice that they are directly comparable due to scaling and centering

# #where to get coefficient estimates:
# summary(lm(summary(piAll_by_species_T4_4.glm[[1]])$bestmodel,
#            data=bySpecies.ls[[1]]))$coefficients[,1]
# summary(lm(summary(get(glm_T4_4[i])[[j]])$bestmodel,
#            data=bySpecies.ls[[1]]))$coefficients[,1]
# collecting object names
glm_T4_4 <- ls(pattern = "_T4_4.glm")
glm_coefficients <- matrix(nrow = 14*7, ncol = 9)
k <- 0
for (i in 1:14)
{
  for (j in 1:7)
  {
    k <- k+1
## NOT USED:
#    tmp <- get(glm_T4_4[i])[[j]]$coefficients
    tmp <- summary(lm(summary(get(glm_T4_4[i])[[j]])$bestmodel,
           data=bySpecies.ls[[j]]))$coefficients[,1]
    glm_coefficients[k,1] <- glm_T4_4[i]
    glm_coefficients[k,2] <- names(get(glm_T4_4[i]))[j]
    glm_coefficients[k,3] <- round(tmp[1], 3)
    if(is.element("AdmixLayer1:latwgs84", names(tmp))) glm_coefficients[k,4] <- round(tmp["AdmixLayer1:latwgs84"], 3)
    if(is.element("AdmixLayer1:lonwgs84", names(tmp))) glm_coefficients[k,5] <- round(tmp["AdmixLayer1:lonwgs84"], 3)
    if(is.element("lonwgs84:latwgs84", names(tmp))) glm_coefficients[k,6] <- round(tmp["lonwgs84:latwgs84"], 3)
    if(is.element("AdmixLayer1", names(tmp))) glm_coefficients[k,7] <- round(tmp["AdmixLayer1"], 3)
    if(is.element("lonwgs84", names(tmp))) glm_coefficients[k,8] <- round(tmp["lonwgs84"], 3)
    if(is.element("latwgs84", names(tmp))) glm_coefficients[k,9] <- round(tmp["latwgs84"], 3)
  }
}
## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.

## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
##   Consider formula(paste(x, collapse = " ")) instead.
glm_coefficients.df <- data.frame(glm_coefficients)
names(glm_coefficients.df) <- c("genDivVar","species","intercept","AdmixCoef x Lat","AdmixCoef x Lon",
                                "Lon x Lat","AdmixCoef","Lon","Lat")
#making variable names human-friendly
glm_coefficients.df[glm_coefficients.df == "mean_f1_by_species_T4_4.glm"] <- "stairway_mean(d(theta)/dt)"
glm_coefficients.df[glm_coefficients.df == "mean_f2_by_species_T4_4.glm"] <- "stairway_mean(d2(theta)/d2t)"
glm_coefficients.df[glm_coefficients.df == "mean_medianTheta_by_species_T4_4.glm"] <- "stairway_mean(theta)"
glm_coefficients.df[glm_coefficients.df == "median_f1_by_species_T4_4.glm"] <- "stairway_median(d(theta)/dt)"
glm_coefficients.df[glm_coefficients.df == "median_f2_by_species_T4_4.glm"] <- "stairway_median(d2(theta)/d2t)"
glm_coefficients.df[glm_coefficients.df == "median_medianTheta_by_species_T4_4.glm"] <- "stairway_median(theta)"
glm_coefficients.df[glm_coefficients.df == "pi_0fold_by_species_T4_4.glm"] <- "pi_0fold"
glm_coefficients.df[glm_coefficients.df == "pi_4fold_by_species_T4_4.glm"] <- "pi_4fold"
glm_coefficients.df[glm_coefficients.df == "pi_putNeu_by_species_T4_4.glm"] <- "pi_putNeu"
glm_coefficients.df[glm_coefficients.df == "pi0_pi4_by_species_T4_4.glm"] <- "pi0/pi4"
glm_coefficients.df[glm_coefficients.df == "piAll_by_species_T4_4.glm"] <- "piAll"
glm_coefficients.df[glm_coefficients.df == "sd_f1_by_species_T4_4.glm"] <- "stairway_stdev(d(theta)/dt)"
glm_coefficients.df[glm_coefficients.df == "sd_f2_by_species_T4_4.glm"] <- "stairway_stdev(d2(theta)/d2t)"
glm_coefficients.df[glm_coefficients.df == "sd_medianTheta_by_species_T4_4.glm"] <- "stairway_stdev(theta)"
write.table(x = glm_coefficients.df, file = "glm_coefficients.txt", row.names = F, col.names = T, quote = F, sep = "\t")

Generating full plots with species and divStat values:

# building an indicator of whether the coeeficient is significant in the full model:
# starting from glm_coefficients.df: keeping relevant lines & cols
labels.df <- glm_coefficients.df[c(1:14,36:42,64:84,92:98), c(1:2,8:9)]
# (re)creating correspondence between line names and column names in allDivEnv.df
labels.df[labels.df == "stairway_median(theta)"] <- names(allDivEnv.df)[104]
labels.df[labels.df == "stairway_mean(d(theta)/dt)"] <- names(allDivEnv.df)[106]
labels.df[labels.df == "stairway_mean(d2(theta)/d2t)"] <- names(allDivEnv.df)[109]
labels.df[labels.df == "stairway_stdev(d(theta)/dt)"] <- names(allDivEnv.df)[108]
labels.df[labels.df == "stairway_stdev(theta)"] <- names(allDivEnv.df)[105]
labels.df[labels.df == "piAll"] <- names(allDivEnv.df)[98]
labels.df[labels.df == "pi0/pi4"] <- names(allDivEnv.df)[102]
labels.df <- cbind(labels.df, glm_coefficients.df[c(1:14,36:42,64:84,92:98), 1])
names(labels.df)[5] <- "genDivVarClean"
#
pdf(file = "divStatVsGeoBySpeciesLAT.pdf", width = 20, height = 20)
par(mfrow = c(7,7))
for (i in c(98,102,104,105,106,108,109))
{
  for (k in levels(as.factor(allDivEnv.df$species[-146])))
  {
  tmp <- subset(allDivEnv.df, species == k)[-146,]
  # checking condition on significance, adding stars
  tmp.labels <- subset(labels.df, species == k & genDivVar == names(tmp)[i])
  if (is.na(tmp.labels[1,4]) == F) Ylab <- paste(tmp.labels[1,5], "(*)", sep = " ") else Ylab <- tmp.labels[1,5]
  plot(tmp[,i] ~ tmp$latwgs84, pch = 21, bg = "grey", xaxt = "n", yaxt = "n", ylab = Ylab, xlab = "latitude", main = k,
       xilm = c(min(allDivEnv.df$latwgs84), max(allDivEnv.df$latwgs84)),
       ylim = c(min(allDivEnv.df[-146,i], na.rm = T), max(allDivEnv.df[-146,i], na.rm = T)))
  }
}
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
dev.off()
## quartz_off_screen 
##                 2
#
pdf(file = "divStatVsGeoBySpeciesLON.pdf", width = 20, height = 20)
par(mfrow = c(7,7))
for (i in c(98,102,104,105,106,108,109))
{
  for (k in levels(as.factor(allDivEnv.df$species[-146])))
  {
  tmp <- subset(allDivEnv.df, species == k)[-146,]
  # checking condition on significance, adding stars
  tmp.labels <- subset(labels.df, species == k & genDivVar == names(tmp)[i])
  if (is.na(tmp.labels[1,3]) == F) Ylab <- paste(tmp.labels[1,5], "(*)", sep = " ") else Ylab <- tmp.labels[1,5]
  plot(tmp[,i] ~ tmp$lonwgs84, pch = 21, bg = "grey", xaxt = "n", yaxt = "n", ylab = Ylab, xlab = "longitude", main = k,
       xilm = c(min(allDivEnv.df$lonwgs84), max(allDivEnv.df$lonwgs84)),
       ylim = c(min(allDivEnv.df[-146,i], na.rm = T), max(allDivEnv.df[-146,i], na.rm = T)))
  }
}
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.window(...): "xilm" n'est pas un paramètre graphique
## Warning in plot.xy(xy, type, ...): "xilm" n'est pas un paramètre graphique
## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique

## Warning in axis(side = side, at = at, labels = labels, ...): "xilm" n'est pas un
## paramètre graphique
## Warning in box(...): "xilm" n'est pas un paramètre graphique
## Warning in title(...): "xilm" n'est pas un paramètre graphique
dev.off()
## quartz_off_screen 
##                 2
# notice that, of course, here one cannot obtain the same slopes as in the full model used above

Part 3. Species-group comparisons

Effect of species group on RAW values

(using normalised values would suppress the effect)

# 1. defining functions
geoGroups <- function(x) if (is.element(x, c("PA","BP","PS"))==T) "boreal" else "temperate"
phyloGroups <- function(x) if (is.element(x, c("PA","PP","PS"))==T) "gymno" else "angio"
# reassembling relevant unscaled data
allDivGeo.raw.df <- merge(x = envDataMeans.df[1:3],piStats.df[-2], by = 0, all.y = T)
row.names(allDivGeo.raw.df)<- allDivGeo.raw.df$Row.names
allDivGeo.raw.df <- allDivGeo.raw.df[-1]
allDivGeo.raw.df <- merge(allDivGeo.raw.df, all.sumstats.df[-1], by = 0, all.y = T)
row.names(allDivGeo.raw.df)<- allDivGeo.raw.df$Row.names
allDivGeo.raw.df <- allDivGeo.raw.df[-1]
# applying functions
allDivGeo.raw.geoGroups <- sapply(X = allDivGeo.raw.df$species, FUN = geoGroups)
allDivGeo.raw.phyloGroups <- sapply(X = allDivGeo.raw.df$species, FUN = phyloGroups)
allDivGeo.raw.df <- cbind(allDivGeo.raw.geoGroups, allDivGeo.raw.phyloGroups, allDivGeo.raw.df)[-which(allDivGeo.raw.df$species == "OB"),]

names(allDivGeo.raw.df)[1:2] <- c("geoGroups","phyloGroups")
allDivGeo.raw.df$geoGroups <- factor(allDivGeo.raw.df$geoGroups)
allDivGeo.raw.df$phyloGroups <- factor(allDivGeo.raw.df$phyloGroups)
allDivGeo.raw.df$species <- factor(allDivGeo.raw.df$species)

Running models (ANOVA):

# testing
options(contrasts=c('contr.sum','contr.sum'))
contrasts(allDivGeo.raw.df$geoGroups) <- c(-0.5,0.5)
contrasts(allDivGeo.raw.df$species) 
##    [,1] [,2] [,3] [,4] [,5] [,6]
## BP    1    0    0    0    0    0
## FS    0    1    0    0    0    0
## PA    0    0    1    0    0    0
## PO    0    0    0    1    0    0
## PP    0    0    0    0    1    0
## PS    0    0    0    0    0    1
## QP   -1   -1   -1   -1   -1   -1
summary(aov(pi_all ~ geoGroups/species, data = allDivGeo.raw.df))
##                    Df    Sum Sq  Mean Sq F value   Pr(>F)    
## geoGroups           1 7.000e-07 7.00e-07   18.53 2.95e-05 ***
## geoGroups:species   5 1.795e-04 3.59e-05  952.98  < 2e-16 ***
## Residuals         156 5.880e-06 4.00e-08                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
effects(aov(pi_all ~ geoGroups/species, data = allDivGeo.raw.df))
##                 (Intercept)                  geoGroups1 
##               -3.437604e-02               -8.354193e-04 
##    geoGroupsboreal:species1 geoGroupstemperate:species1 
##               -3.199672e-03                1.134956e-02 
## geoGroupstemperate:species2    geoGroupsboreal:species3 
##                4.549994e-03               -4.121778e-03 
## geoGroupstemperate:species4                             
##               -1.660154e-03               -3.184523e-05 
##                                                         
##                1.127230e-05               -3.845758e-04 
##                                                         
##               -3.815865e-04               -1.454860e-04 
##                                                         
##               -3.475684e-05                1.046131e-04 
##                                                         
##                2.420948e-04               -1.646756e-04 
##                                                         
##               -1.606914e-04               -4.016890e-05 
##                                                         
##               -5.670911e-05                4.188323e-05 
##                                                         
##                6.406339e-06                4.921999e-05 
##                                                         
##                3.955862e-05               -1.122587e-04 
##                                                         
##               -2.562893e-04                1.500694e-05 
##                                                         
##               -2.338329e-05               -7.173558e-05 
##                                                         
##               -1.545235e-04                1.752920e-04 
##                                                         
##                9.571991e-05                4.201323e-04 
##                                                         
##               -5.887968e-05               -4.793393e-05 
##                                                         
##               -2.400464e-04               -2.738791e-04 
##                                                         
##                1.675410e-04                3.527013e-04 
##                                                         
##                3.806537e-04                3.590371e-04 
##                                                         
##                1.473222e-04                1.562395e-04 
##                                                         
##                1.476028e-04                1.227030e-04 
##                                                         
##                1.848759e-04                2.222841e-04 
##                                                         
##               -1.827190e-05                6.346906e-05 
##                                                         
##                4.560770e-05                6.267649e-05 
##                                                         
##               -3.836787e-05               -5.606951e-07 
##                                                         
##                4.310991e-04                3.624116e-04 
##                                                         
##               -1.133874e-04               -3.085951e-04 
##                                                         
##                9.837450e-05                2.680981e-06 
##                                                         
##               -1.050922e-04               -5.901771e-05 
##                                                         
##               -5.823180e-05                5.965829e-05 
##                                                         
##                7.259521e-05               -6.122726e-04 
##                                                         
##                2.624607e-04                1.015907e-04 
##                                                         
##                2.878589e-04                1.677725e-04 
##                                                         
##                3.237854e-05                2.913715e-04 
##                                                         
##                2.826790e-05               -1.320552e-05 
##                                                         
##                1.539381e-05               -1.457908e-04 
##                                                         
##               -1.276661e-04                1.233366e-05 
##                                                         
##               -3.415997e-05                2.631221e-05 
##                                                         
##                1.286155e-04                1.276810e-05 
##                                                         
##               -2.255241e-04               -3.433461e-04 
##                                                         
##               -3.754001e-05               -1.069760e-04 
##                                                         
##               -1.502020e-04               -1.308831e-04 
##                                                         
##               -2.739286e-04                1.688055e-04 
##                                                         
##                5.125668e-05               -2.110159e-05 
##                                                         
##                2.559927e-04               -1.530736e-04 
##                                                         
##               -1.459765e-04                1.418593e-04 
##                                                         
##                1.395252e-04               -4.027643e-04 
##                                                         
##               -8.987361e-05                1.683631e-04 
##                                                         
##                1.405098e-04               -2.720947e-04 
##                                                         
##               -2.337527e-04                6.986189e-05 
##                                                         
##                9.121452e-05                1.964975e-04 
##                                                         
##                1.421349e-04               -2.038777e-04 
##                                                         
##               -1.237844e-04               -1.686162e-04 
##                                                         
##               -1.779475e-04               -1.547538e-04 
##                                                         
##               -4.391958e-05                2.683465e-05 
##                                                         
##               -7.909757e-05               -4.911300e-04 
##                                                         
##               -3.167330e-04               -1.665555e-04 
##                                                         
##               -1.850987e-04               -6.533562e-05 
##                                                         
##               -1.176700e-04                1.123326e-04 
##                                                         
##                1.627884e-04               -2.023571e-04 
##                                                         
##               -1.327043e-04               -2.945396e-04 
##                                                         
##               -1.781894e-04               -7.226876e-06 
##                                                         
##               -1.072259e-05               -9.307273e-05 
##                                                         
##                4.446548e-05               -3.664236e-04 
##                                                         
##               -3.853351e-04                2.480223e-04 
##                                                         
##                3.599447e-04                1.992351e-04 
##                                                         
##               -5.933843e-05               -1.419748e-04 
##                                                         
##               -1.060551e-05                1.171120e-04 
##                                                         
##                1.336149e-04                2.218081e-04 
##                                                         
##                9.906385e-05               -3.209864e-05 
##                                                         
##                1.227392e-04               -1.936711e-05 
##                                                         
##                2.997017e-04                2.660171e-04 
##                                                         
##                4.490699e-04                3.244390e-04 
##                                                         
##               -3.941574e-05               -1.731951e-04 
##                                                         
##                4.129331e-05                6.455946e-05 
##                                                         
##               -6.328115e-05               -8.475174e-05 
##                                                         
##                2.939691e-04                2.901112e-04 
##                                                         
##               -1.100342e-04                3.717123e-04 
##                                                         
##                3.967414e-07                9.266596e-06 
##                                                         
##                6.018668e-05                7.527676e-05 
##                             
##               -5.216126e-05 
## attr(,"assign")
##  [1] 0 1 2 2 2 2 2 2 2 2 2 2 2 2
## attr(,"class")
## [1] "coef"
summary(aov(pi_all ~ phyloGroups/species, data = allDivGeo.raw.df, contrasts = "contr.sum"))
## Warning in model.matrix.default(mt, mf, contrasts): non-list contrasts argument
## ignored
##                      Df    Sum Sq   Mean Sq F value Pr(>F)    
## phyloGroups           1 1.012e-05 1.012e-05   268.6 <2e-16 ***
## phyloGroups:species   5 1.701e-04 3.402e-05   903.0 <2e-16 ***
## Residuals           156 5.880e-06 4.000e-08                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(aov(pi_all ~ geoGroups/species, data = allDivGeo.raw.df))

TukeyHSD(aov(pi_all ~ geoGroups/species, data = allDivGeo.raw.df)) 
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = pi_all ~ geoGroups/species, data = allDivGeo.raw.df)
## 
## $geoGroups
##                        diff          lwr          upr    p adj
## temperate-boreal 0.00013197 7.140754e-05 0.0001925325 2.95e-05
## 
## $`geoGroups:species`
##                                    diff           lwr           upr     p adj
## temperate:BP-boreal:BP               NA            NA            NA        NA
## boreal:FS-boreal:BP                  NA            NA            NA        NA
## temperate:FS-boreal:BP     7.928675e-04  6.035559e-04  0.0009821790 0.0000000
## boreal:PA-boreal:BP        1.382063e-03  1.190982e-03  0.0015731430 0.0000000
## temperate:PA-boreal:BP               NA            NA            NA        NA
## boreal:PO-boreal:BP                  NA            NA            NA        NA
## temperate:PO-boreal:BP    -6.107229e-05 -2.582967e-04  0.0001361521 0.9986730
## boreal:PP-boreal:BP                  NA            NA            NA        NA
## temperate:PP-boreal:BP    -5.463787e-04 -7.374592e-04 -0.0003552982 0.0000000
## boreal:PS-boreal:BP        1.911734e-04 -3.847357e-06  0.0003861941 0.0607936
## temperate:PS-boreal:BP               NA            NA            NA        NA
## boreal:QP-boreal:BP                  NA            NA            NA        NA
## temperate:QP-boreal:BP     2.999910e-03  2.794882e-03  0.0032049380 0.0000000
## boreal:FS-temperate:BP               NA            NA            NA        NA
## temperate:FS-temperate:BP            NA            NA            NA        NA
## boreal:PA-temperate:BP               NA            NA            NA        NA
## temperate:PA-temperate:BP            NA            NA            NA        NA
## boreal:PO-temperate:BP               NA            NA            NA        NA
## temperate:PO-temperate:BP            NA            NA            NA        NA
## boreal:PP-temperate:BP               NA            NA            NA        NA
## temperate:PP-temperate:BP            NA            NA            NA        NA
## boreal:PS-temperate:BP               NA            NA            NA        NA
## temperate:PS-temperate:BP            NA            NA            NA        NA
## boreal:QP-temperate:BP               NA            NA            NA        NA
## temperate:QP-temperate:BP            NA            NA            NA        NA
## temperate:FS-boreal:FS               NA            NA            NA        NA
## boreal:PA-boreal:FS                  NA            NA            NA        NA
## temperate:PA-boreal:FS               NA            NA            NA        NA
## boreal:PO-boreal:FS                  NA            NA            NA        NA
## temperate:PO-boreal:FS               NA            NA            NA        NA
## boreal:PP-boreal:FS                  NA            NA            NA        NA
## temperate:PP-boreal:FS               NA            NA            NA        NA
## boreal:PS-boreal:FS                  NA            NA            NA        NA
## temperate:PS-boreal:FS               NA            NA            NA        NA
## boreal:QP-boreal:FS                  NA            NA            NA        NA
## temperate:QP-boreal:FS               NA            NA            NA        NA
## boreal:PA-temperate:FS     5.891950e-04  4.039451e-04  0.0007744450 0.0000000
## temperate:PA-temperate:FS            NA            NA            NA        NA
## boreal:PO-temperate:FS               NA            NA            NA        NA
## temperate:PO-temperate:FS -8.539398e-04 -1.045521e-03 -0.0006623589 0.0000000
## boreal:PP-temperate:FS               NA            NA            NA        NA
## temperate:PP-temperate:FS -1.339246e-03 -1.524496e-03 -0.0011539962 0.0000000
## boreal:PS-temperate:FS    -6.016941e-04 -7.910057e-04 -0.0004123826 0.0000000
## temperate:PS-temperate:FS            NA            NA            NA        NA
## boreal:QP-temperate:FS               NA            NA            NA        NA
## temperate:QP-temperate:FS  2.207042e-03  2.007437e-03  0.0024066478 0.0000000
## temperate:PA-boreal:PA               NA            NA            NA        NA
## boreal:PO-boreal:PA                  NA            NA            NA        NA
## temperate:PO-boreal:PA    -1.443135e-03 -1.636464e-03 -0.0012498057 0.0000000
## boreal:PP-boreal:PA                  NA            NA            NA        NA
## temperate:PP-boreal:PA    -1.928441e-03 -2.115498e-03 -0.0017413839 0.0000000
## boreal:PS-boreal:PA       -1.190889e-03 -1.381970e-03 -0.0009998087 0.0000000
## temperate:PS-boreal:PA               NA            NA            NA        NA
## boreal:QP-boreal:PA                  NA            NA            NA        NA
## temperate:QP-boreal:PA     1.617847e-03  1.416563e-03  0.0018191313 0.0000000
## boreal:PO-temperate:PA               NA            NA            NA        NA
## temperate:PO-temperate:PA            NA            NA            NA        NA
## boreal:PP-temperate:PA               NA            NA            NA        NA
## temperate:PP-temperate:PA            NA            NA            NA        NA
## boreal:PS-temperate:PA               NA            NA            NA        NA
## temperate:PS-temperate:PA            NA            NA            NA        NA
## boreal:QP-temperate:PA               NA            NA            NA        NA
## temperate:QP-temperate:PA            NA            NA            NA        NA
## temperate:PO-boreal:PO               NA            NA            NA        NA
## boreal:PP-boreal:PO                  NA            NA            NA        NA
## temperate:PP-boreal:PO               NA            NA            NA        NA
## boreal:PS-boreal:PO                  NA            NA            NA        NA
## temperate:PS-boreal:PO               NA            NA            NA        NA
## boreal:QP-boreal:PO                  NA            NA            NA        NA
## temperate:QP-boreal:PO               NA            NA            NA        NA
## boreal:PP-temperate:PO               NA            NA            NA        NA
## temperate:PP-temperate:PO -4.853064e-04 -6.786355e-04 -0.0002919773 0.0000000
## boreal:PS-temperate:PO     2.522456e-04  5.502124e-05  0.0004494701 0.0018747
## temperate:PS-temperate:PO            NA            NA            NA        NA
## boreal:QP-temperate:PO               NA            NA            NA        NA
## temperate:QP-temperate:PO  3.060982e-03  2.853857e-03  0.0032681075 0.0000000
## temperate:PP-boreal:PP               NA            NA            NA        NA
## boreal:PS-boreal:PP                  NA            NA            NA        NA
## temperate:PS-boreal:PP               NA            NA            NA        NA
## boreal:QP-boreal:PP                  NA            NA            NA        NA
## temperate:QP-boreal:PP               NA            NA            NA        NA
## boreal:PS-temperate:PP     7.375520e-04  5.464715e-04  0.0009286325 0.0000000
## temperate:PS-temperate:PP            NA            NA            NA        NA
## boreal:QP-temperate:PP               NA            NA            NA        NA
## temperate:QP-temperate:PP  3.546288e-03  3.345005e-03  0.0037475725 0.0000000
## temperate:PS-boreal:PS               NA            NA            NA        NA
## boreal:QP-boreal:PS                  NA            NA            NA        NA
## temperate:QP-boreal:PS     2.808736e-03  2.603708e-03  0.0030137646 0.0000000
## boreal:QP-temperate:PS               NA            NA            NA        NA
## temperate:QP-temperate:PS            NA            NA            NA        NA
## temperate:QP-boreal:QP               NA            NA            NA        NA
TukeyHSD(aov(pi_all ~ phyloGroups/species, data = allDivGeo.raw.df))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = pi_all ~ phyloGroups/species, data = allDivGeo.raw.df)
## 
## $phyloGroups
##                      diff           lwr           upr p adj
## gymno-angio -0.0005010103 -0.0005613974 -0.0004406233     0
## 
## $`phyloGroups:species`
##                            diff           lwr           upr     p adj
## gymno:BP-angio:BP            NA            NA            NA        NA
## angio:FS-angio:BP  7.928675e-04  6.035559e-04  0.0009821790 0.0000000
## gymno:FS-angio:BP            NA            NA            NA        NA
## angio:PA-angio:BP            NA            NA            NA        NA
## gymno:PA-angio:BP  1.382063e-03  1.190982e-03  0.0015731430 0.0000000
## angio:PO-angio:BP -6.107229e-05 -2.582967e-04  0.0001361521 0.9986730
## gymno:PO-angio:BP            NA            NA            NA        NA
## angio:PP-angio:BP            NA            NA            NA        NA
## gymno:PP-angio:BP -5.463787e-04 -7.374592e-04 -0.0003552982 0.0000000
## angio:PS-angio:BP            NA            NA            NA        NA
## gymno:PS-angio:BP  1.911734e-04 -3.847357e-06  0.0003861941 0.0607936
## angio:QP-angio:BP  2.999910e-03  2.794882e-03  0.0032049380 0.0000000
## gymno:QP-angio:BP            NA            NA            NA        NA
## angio:FS-gymno:BP            NA            NA            NA        NA
## gymno:FS-gymno:BP            NA            NA            NA        NA
## angio:PA-gymno:BP            NA            NA            NA        NA
## gymno:PA-gymno:BP            NA            NA            NA        NA
## angio:PO-gymno:BP            NA            NA            NA        NA
## gymno:PO-gymno:BP            NA            NA            NA        NA
## angio:PP-gymno:BP            NA            NA            NA        NA
## gymno:PP-gymno:BP            NA            NA            NA        NA
## angio:PS-gymno:BP            NA            NA            NA        NA
## gymno:PS-gymno:BP            NA            NA            NA        NA
## angio:QP-gymno:BP            NA            NA            NA        NA
## gymno:QP-gymno:BP            NA            NA            NA        NA
## gymno:FS-angio:FS            NA            NA            NA        NA
## angio:PA-angio:FS            NA            NA            NA        NA
## gymno:PA-angio:FS  5.891950e-04  4.039451e-04  0.0007744450 0.0000000
## angio:PO-angio:FS -8.539398e-04 -1.045521e-03 -0.0006623589 0.0000000
## gymno:PO-angio:FS            NA            NA            NA        NA
## angio:PP-angio:FS            NA            NA            NA        NA
## gymno:PP-angio:FS -1.339246e-03 -1.524496e-03 -0.0011539962 0.0000000
## angio:PS-angio:FS            NA            NA            NA        NA
## gymno:PS-angio:FS -6.016941e-04 -7.910057e-04 -0.0004123826 0.0000000
## angio:QP-angio:FS  2.207042e-03  2.007437e-03  0.0024066478 0.0000000
## gymno:QP-angio:FS            NA            NA            NA        NA
## angio:PA-gymno:FS            NA            NA            NA        NA
## gymno:PA-gymno:FS            NA            NA            NA        NA
## angio:PO-gymno:FS            NA            NA            NA        NA
## gymno:PO-gymno:FS            NA            NA            NA        NA
## angio:PP-gymno:FS            NA            NA            NA        NA
## gymno:PP-gymno:FS            NA            NA            NA        NA
## angio:PS-gymno:FS            NA            NA            NA        NA
## gymno:PS-gymno:FS            NA            NA            NA        NA
## angio:QP-gymno:FS            NA            NA            NA        NA
## gymno:QP-gymno:FS            NA            NA            NA        NA
## gymno:PA-angio:PA            NA            NA            NA        NA
## angio:PO-angio:PA            NA            NA            NA        NA
## gymno:PO-angio:PA            NA            NA            NA        NA
## angio:PP-angio:PA            NA            NA            NA        NA
## gymno:PP-angio:PA            NA            NA            NA        NA
## angio:PS-angio:PA            NA            NA            NA        NA
## gymno:PS-angio:PA            NA            NA            NA        NA
## angio:QP-angio:PA            NA            NA            NA        NA
## gymno:QP-angio:PA            NA            NA            NA        NA
## angio:PO-gymno:PA -1.443135e-03 -1.636464e-03 -0.0012498057 0.0000000
## gymno:PO-gymno:PA            NA            NA            NA        NA
## angio:PP-gymno:PA            NA            NA            NA        NA
## gymno:PP-gymno:PA -1.928441e-03 -2.115498e-03 -0.0017413839 0.0000000
## angio:PS-gymno:PA            NA            NA            NA        NA
## gymno:PS-gymno:PA -1.190889e-03 -1.381970e-03 -0.0009998087 0.0000000
## angio:QP-gymno:PA  1.617847e-03  1.416563e-03  0.0018191313 0.0000000
## gymno:QP-gymno:PA            NA            NA            NA        NA
## gymno:PO-angio:PO            NA            NA            NA        NA
## angio:PP-angio:PO            NA            NA            NA        NA
## gymno:PP-angio:PO -4.853064e-04 -6.786355e-04 -0.0002919773 0.0000000
## angio:PS-angio:PO            NA            NA            NA        NA
## gymno:PS-angio:PO  2.522456e-04  5.502124e-05  0.0004494701 0.0018747
## angio:QP-angio:PO  3.060982e-03  2.853857e-03  0.0032681075 0.0000000
## gymno:QP-angio:PO            NA            NA            NA        NA
## angio:PP-gymno:PO            NA            NA            NA        NA
## gymno:PP-gymno:PO            NA            NA            NA        NA
## angio:PS-gymno:PO            NA            NA            NA        NA
## gymno:PS-gymno:PO            NA            NA            NA        NA
## angio:QP-gymno:PO            NA            NA            NA        NA
## gymno:QP-gymno:PO            NA            NA            NA        NA
## gymno:PP-angio:PP            NA            NA            NA        NA
## angio:PS-angio:PP            NA            NA            NA        NA
## gymno:PS-angio:PP            NA            NA            NA        NA
## angio:QP-angio:PP            NA            NA            NA        NA
## gymno:QP-angio:PP            NA            NA            NA        NA
## angio:PS-gymno:PP            NA            NA            NA        NA
## gymno:PS-gymno:PP  7.375520e-04  5.464715e-04  0.0009286325 0.0000000
## angio:QP-gymno:PP  3.546288e-03  3.345005e-03  0.0037475725 0.0000000
## gymno:QP-gymno:PP            NA            NA            NA        NA
## gymno:PS-angio:PS            NA            NA            NA        NA
## angio:QP-angio:PS            NA            NA            NA        NA
## gymno:QP-angio:PS            NA            NA            NA        NA
## angio:QP-gymno:PS  2.808736e-03  2.603708e-03  0.0030137646 0.0000000
## gymno:QP-gymno:PS            NA            NA            NA        NA
## gymno:QP-angio:QP            NA            NA            NA        NA
proportion <- table(allDivGeo.raw.df$species) / nrow(allDivGeo.raw.df)
boxplot(allDivGeo.raw.df$pi_all ~ allDivGeo.raw.df$species, col = c("blue", "green", "blue", "green", "green", "blue","green"),
        width=proportion)

summary(glm(pi_all ~ latwgs84 * species, data = allDivGeo.raw.df))
## 
## Call:
## glm(formula = pi_all ~ latwgs84 * species, data = allDivGeo.raw.df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -3.705e-04  -9.182e-05   8.440e-06   9.133e-05   3.754e-04  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        2.694e-03  1.096e-04  24.576  < 2e-16 ***
## latwgs84           1.598e-07  2.349e-06   0.068  0.94584    
## species1          -9.049e-04  2.251e-04  -4.020 9.23e-05 ***
## species2           8.939e-04  2.677e-04   3.339  0.00106 ** 
## species3          -8.973e-04  2.143e-04  -4.186 4.83e-05 ***
## species4          -2.680e-04  3.430e-04  -0.781  0.43589    
## species5          -2.815e-04  3.336e-04  -0.844  0.40016    
## species6           6.313e-05  2.051e-04   0.308  0.75867    
## latwgs84:species1  5.142e-06  4.362e-06   1.179  0.24034    
## latwgs84:species2 -1.529e-05  5.585e-06  -2.737  0.00695 ** 
## latwgs84:species3  3.114e-05  4.171e-06   7.465 6.45e-12 ***
## latwgs84:species4 -9.342e-06  7.504e-06  -1.245  0.21510    
## latwgs84:species5 -2.185e-05  8.011e-06  -2.727  0.00716 ** 
## latwgs84:species6 -9.663e-06  4.034e-06  -2.395  0.01784 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 2.43043e-08)
## 
##     Null deviance: 1.8607e-04  on 162  degrees of freedom
## Residual deviance: 3.6213e-06  on 149  degrees of freedom
## AIC: -2379.9
## 
## Number of Fisher Scoring iterations: 2
pi_all.glm <- glm(pi_all ~ latwgs84 * species, data = allDivGeo.raw.df)
# p <- ggplot(allDivGeo.raw.df, aes(x=latwgs84, y=pi_all, colour=species)) +
#   geom_point(size=3) +
#   #geom_line(aes(y=predict(pi_all.glm), group=Subject, size="Subjects")) +
#   geom_line(data=newdat, aes(y=predict(fm2, level=0, newdata=newdat), size="Population")) +
#   scale_size_manual(name="Predictions", values=c("Subjects"=0.5, "Population"=3)) +
#   theme_bw(base_size=22)
#defining generic function
divGLM <- function (x, y)
{
  tmp <- list()
  tmp[[1]] <- list(
             glm(x ~ y$latwgs84 * y$species),
             lm(x[which(y$species == head(levels(y$species),1))] ~ 
                  latwgs84, data = subset(y, species == head(levels(y$species),1))),
             lm(x[which(y$geoGroups == "boreal")] ~ 
                  subset(y, geoGroups == "boreal")$latwgs84),
             lm(x[which(y$geoGroups == "temperate")] ~ 
                  subset(y, geoGroups == "temperate")$latwgs84),
             lm(x[which(y$phyloGroups == "gymno")] ~ 
                  subset(y, phyloGroups == "gymno")$latwgs84),
             lm(x[which(y$phyloGroups == "angio")] ~ 
                  subset(y, phyloGroups == "angio")$latwgs84))
 names(tmp[[1]]) <- c("Sp_glm", "lastSp_lm", "geo_glm", "lastGeoGroup_glm", "phylo_glm", "lastPhyloGroup_glm")
 tmp[[2]] <- list(
             glm(x ~ y$lonwgs84 * y$species),
             lm(x[which(y$species == head(levels(y$species),1))] ~ 
                  lonwgs84, data = subset(y, species == head(levels(y$species),1))),
             lm(x[which(y$geoGroups == "boreal")] ~ 
                  subset(y, geoGroups == "boreal")$lonwgs84),
             lm(x[which(y$geoGroups == "temperate")] ~ 
                  subset(y, geoGroups == "temperate")$lonwgs84),
             lm(x[which(y$phyloGroups == "gymno")] ~ 
                  subset(y, phyloGroups == "gymno")$lonwgs84),
             lm(x[which(y$phyloGroups == "angio")] ~ 
                  subset(y, phyloGroups == "angio")$lonwgs84))
 names(tmp[[2]]) <- c("Sp_glm", "lastSp_lm", "geo_glm", "lastGeoGroup_glm", "phylo_glm", "lastPhyloGroup_glm")
 return(tmp)
}
GLMs_forPlotting <- apply(X = allDivGeo.raw.df[c(7,11,13,14,15,17,18)], MARGIN = 2, FUN = divGLM, y = allDivGeo.raw.df)

Plotting of raw dependencies of genDiv variables on Lat, Log by species and species group

Pi_all: L 965

Pi0/Pi4: L 1205

median_medianTheta: L 1450

sd_medianTheta: L 1700

mean_f1: L 1950

sd_f1: L 2200

mean_f2: L 2450

Pi_all

pdf(file = "Pi_all_latBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$latwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$pi_all, 
     xlim = c(min(allDivGeo.raw.df$latwgs84), max(allDivGeo.raw.df$latwgs84)),
     ylim = c(min(allDivGeo.raw.df$pi_all), max(allDivGeo.raw.df$pi_all)),
     col = "blue",
     xlab = "Latitude", ylab = "pi_all", main = "pi_all vs Lat")
# model for first species
curve((GLMs_forPlotting[[1]][[1]][[2]]$coefficients["latwgs84"])*x+
       GLMs_forPlotting[[1]][[1]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$pi_all, 
       col = "green")
curve((GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$pi_all, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$pi_all, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$pi_all, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$pi_all, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$pi_all, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[1]][[1]][[1]]$coefficients["y$latwgs84"])*x+
#        GLMs_forPlotting[[1]][[1]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$latwgs84), 
#       to = max(allDivGeo.raw.df$latwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(57,59,65,49,33,38,35,65,60,62,35),
     y = c(0.0017,0.003,0.0042,0.0015,0.002,0.0025,0.005,0.0026,0.0042,0.0033,0.003),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
# geo group models
curve((GLMs_forPlotting[[1]][[1]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[1]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[1]][[1]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[1]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[1]][[1]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[1]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[1]][[1]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[1]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2
labelData_piAll_lon.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(35,20,60,22,18,66,27,80,14,67,52),
     y = c(0.002,0.003,0.0042,0.0017,0.0013,0.002,0.005,0.003,0.0027,0.0036,0.0025),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
View(labelData_piAll_lon.df)
pdf(file = "Pi_all_lonBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$lonwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$pi_all, 
     xlim = c(min(allDivGeo.raw.df$lonwgs84), max(allDivGeo.raw.df$lonwgs84)),
     ylim = c(min(allDivGeo.raw.df$pi_all), max(allDivGeo.raw.df$pi_all)),
     col = "blue",
     xlab = "Longitude", ylab = "pi_all", main = "pi_all vs Lon")
# model for first species
curve((GLMs_forPlotting[[1]][[2]][[2]]$coefficients["lonwgs84"])*x+
       GLMs_forPlotting[[1]][[2]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$pi_all, 
       col = "green")
curve((GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$pi_all, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$pi_all, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$pi_all, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$pi_all, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$pi_all, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[1]][[2]][[1]]$coefficients["y$lonwgs84"])*x+
#        GLMs_forPlotting[[1]][[2]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$lonwgs84), 
#       to = max(allDivGeo.raw.df$lonwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_piAll_lon.df$names,
     x = labelData_piAll_lon.df$x,
     y = labelData_piAll_lon.df$y,
     col = labelData_piAll_lon.df$col)
# geo group models
curve((GLMs_forPlotting[[1]][[2]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[2]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[1]][[2]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[2]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[1]][[2]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[2]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[1]][[2]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[1]][[2]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2

Pi0/Pi4

labelData_pi0_pi4_lat.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(40,62,40,50,45,37,35,60,34,32,32),
     y = c(0.2,0.24,0.35,0.275,0.3,0.45,0.23,0.31,0.26,0.31,0.24),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato")
     )
View(labelData_pi0_pi4_lat.df)
pdf(file = "pi0_pi4_latBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$latwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$pi0_pi4, 
     xlim = c(min(allDivGeo.raw.df$latwgs84), max(allDivGeo.raw.df$latwgs84)),
     ylim = c(min(allDivGeo.raw.df$pi0_pi4), max(allDivGeo.raw.df$pi0_pi4)),
     col = "blue",
     xlab = "Latitude", ylab = "pi0_pi4", main = "pi0_pi4 vs Lat")
# model for first species
curve((GLMs_forPlotting[[2]][[1]][[2]]$coefficients["latwgs84"])*x+
       GLMs_forPlotting[[2]][[1]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$pi0_pi4, 
       col = "green")
curve((GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$pi0_pi4, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$pi0_pi4, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$pi0_pi4, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$pi0_pi4, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$pi0_pi4, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[2]][[1]][[1]]$coefficients["y$latwgs84"])*x+
#        GLMs_forPlotting[[2]][[1]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$latwgs84), 
#       to = max(allDivGeo.raw.df$latwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_pi0_pi4_lat.df$names,
     x = labelData_pi0_pi4_lat.df$x,
     y = labelData_pi0_pi4_lat.df$y,
     col = labelData_pi0_pi4_lat.df$col)
# geo group models
curve((GLMs_forPlotting[[2]][[1]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[1]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[2]][[1]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[1]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[2]][[1]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[1]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[2]][[1]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[1]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2
labelData_pi0_pi4_lon.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(35,-5,62,-8,18,50,27,80,24,50,40),
     y = c(0.19,0.225,0.35,0.26,0.29,0.44,0.24,0.32,0.23,0.38,0.22),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
View(labelData_pi0_pi4_lon.df)
pdf(file = "pi0_pi4_lonBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$lonwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$pi0_pi4, 
     xlim = c(min(allDivGeo.raw.df$lonwgs84), max(allDivGeo.raw.df$lonwgs84)),
     ylim = c(min(allDivGeo.raw.df$pi0_pi4), max(allDivGeo.raw.df$pi0_pi4)),
     col = "blue",
     xlab = "Longitude", ylab = "pi0_pi4", main = "pi0_pi4 vs Lon")
# model for first species
curve((GLMs_forPlotting[[2]][[2]][[2]]$coefficients["lonwgs84"])*x+
       GLMs_forPlotting[[2]][[2]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$pi0_pi4, 
       col = "green")
curve((GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$pi0_pi4, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$pi0_pi4, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$pi0_pi4, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$pi0_pi4, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$pi0_pi4, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[2]][[2]][[1]]$coefficients["y$lonwgs84"])*x+
#        GLMs_forPlotting[[2]][[2]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$lonwgs84), 
#       to = max(allDivGeo.raw.df$lonwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_pi0_pi4_lon.df$names,
     x = labelData_pi0_pi4_lon.df$x,
     y = labelData_pi0_pi4_lon.df$y,
     col = labelData_pi0_pi4_lon.df$col)
# geo group models
curve((GLMs_forPlotting[[2]][[2]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[2]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[2]][[2]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[2]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[2]][[2]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[2]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[2]][[2]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[2]][[2]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2

median_medianTheta

labelData_median_medianTheta_lat.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(67,50,62,50,40,66,36,38,33,32,32),
     y = c(0.007,0.0035,0.0085,0.001,0.0012,0.0028,0.009,0.005,0.001,0.0025,0.0035),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato")
     )
View(labelData_median_medianTheta_lat.df)
pdf(file = "median_medianTheta_latBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$latwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$median_medianTheta, 
     xlim = c(min(allDivGeo.raw.df$latwgs84), max(allDivGeo.raw.df$latwgs84)),
     ylim = c(min(allDivGeo.raw.df$median_medianTheta), max(allDivGeo.raw.df$median_medianTheta)),
     col = "blue",
     xlab = "Latitude", ylab = "median_medianTheta", main = "median_medianTheta vs Lat")
# model for first species
curve((GLMs_forPlotting[[3]][[1]][[2]]$coefficients["latwgs84"])*x+
       GLMs_forPlotting[[3]][[1]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$median_medianTheta, 
       col = "green")
curve((GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$median_medianTheta, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$median_medianTheta, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$median_medianTheta, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$median_medianTheta, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$median_medianTheta, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[3]][[1]][[1]]$coefficients["y$latwgs84"])*x+
#        GLMs_forPlotting[[3]][[1]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$latwgs84), 
#       to = max(allDivGeo.raw.df$latwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_median_medianTheta_lat.df$names,
     x = labelData_median_medianTheta_lat.df$x,
     y = labelData_median_medianTheta_lat.df$y,
     col = labelData_median_medianTheta_lat.df$col)
# geo group models
curve((GLMs_forPlotting[[3]][[1]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[1]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[3]][[1]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[1]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[3]][[1]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[1]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[3]][[1]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[1]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2
labelData_median_medianTheta_lon.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(75,19,40,15,5,50,0,80,23,70,-8),
     y = c(0.0045,0.003,0.0085,0.002,0.0012,0.0015,0.0095,0.0055,0.0042,0.0075,0.0047),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
View(labelData_median_medianTheta_lon.df)
pdf(file = "median_medianTheta_lonBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$lonwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$median_medianTheta, 
     xlim = c(min(allDivGeo.raw.df$lonwgs84), max(allDivGeo.raw.df$lonwgs84)),
     ylim = c(min(allDivGeo.raw.df$median_medianTheta), max(allDivGeo.raw.df$median_medianTheta)),
     col = "blue",
     xlab = "Longitude", ylab = "median_medianTheta", main = "median_medianTheta vs Lon")
# model for first species
curve((GLMs_forPlotting[[3]][[2]][[2]]$coefficients["lonwgs84"])*x+
       GLMs_forPlotting[[3]][[2]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$median_medianTheta, 
       col = "green")
curve((GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$median_medianTheta, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$median_medianTheta, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$median_medianTheta, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$median_medianTheta, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$median_medianTheta, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[3]][[2]][[1]]$coefficients["y$lonwgs84"])*x+
#        GLMs_forPlotting[[3]][[2]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$lonwgs84), 
#       to = max(allDivGeo.raw.df$lonwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_median_medianTheta_lon.df$names,
     x = labelData_median_medianTheta_lon.df$x,
     y = labelData_median_medianTheta_lon.df$y,
     col = labelData_median_medianTheta_lon.df$col)
# geo group models
curve((GLMs_forPlotting[[3]][[2]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[2]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[3]][[2]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[2]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[3]][[2]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[2]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[3]][[2]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[3]][[2]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2

sd_medianTheta

labelData_sd_medianTheta_lat.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(0,0,0,0,0,38,0,38,0,38,0),
     y = c(0,0,0,0,0,0.0175,0,0.0075,0,0.006,0),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato")
     )
View(labelData_sd_medianTheta_lat.df)
pdf(file = "sd_medianTheta_latBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$latwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$sd_medianTheta, 
     xlim = c(min(allDivGeo.raw.df$latwgs84), max(allDivGeo.raw.df$latwgs84)),
     ylim = c(min(allDivGeo.raw.df$sd_medianTheta), max(allDivGeo.raw.df$sd_medianTheta)),
     col = "blue",
     xlab = "Latitude", ylab = "sd_medianTheta", main = "sd_medianTheta vs Lat")
# model for first species
curve((GLMs_forPlotting[[4]][[1]][[2]]$coefficients["latwgs84"])*x+
       GLMs_forPlotting[[4]][[1]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$sd_medianTheta, 
       col = "green")
curve((GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$sd_medianTheta, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$sd_medianTheta, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$sd_medianTheta, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$sd_medianTheta, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$sd_medianTheta, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[4]][[1]][[1]]$coefficients["y$latwgs84"])*x+
#        GLMs_forPlotting[[4]][[1]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$latwgs84), 
#       to = max(allDivGeo.raw.df$latwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_sd_medianTheta_lat.df$names,
     x = labelData_sd_medianTheta_lat.df$x,
     y = labelData_sd_medianTheta_lat.df$y,
     col = labelData_sd_medianTheta_lat.df$col)
# geo group models
curve((GLMs_forPlotting[[4]][[1]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[1]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[4]][[1]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[1]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[4]][[1]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[1]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[4]][[1]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[1]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2
labelData_sd_medianTheta_lon.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(100,100,100,100,100,38,100,0,100,0,100),
     y = c(0,0,0,0,0,0.0125,0,0.009,0,0.005,0),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
View(labelData_sd_medianTheta_lon.df)
pdf(file = "sd_medianTheta_lonBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$lonwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$sd_medianTheta, 
     xlim = c(min(allDivGeo.raw.df$lonwgs84), max(allDivGeo.raw.df$lonwgs84)),
     ylim = c(min(allDivGeo.raw.df$sd_medianTheta), max(allDivGeo.raw.df$sd_medianTheta)),
     col = "blue",
     xlab = "Longitude", ylab = "sd_medianTheta", main = "sd_medianTheta vs Lon")
# model for first species
curve((GLMs_forPlotting[[4]][[2]][[2]]$coefficients["lonwgs84"])*x+
       GLMs_forPlotting[[4]][[2]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$sd_medianTheta, 
       col = "green")
curve((GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$sd_medianTheta, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$sd_medianTheta, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$sd_medianTheta, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$sd_medianTheta, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$sd_medianTheta, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[4]][[2]][[1]]$coefficients["y$lonwgs84"])*x+
#        GLMs_forPlotting[[4]][[2]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$lonwgs84), 
#       to = max(allDivGeo.raw.df$lonwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_sd_medianTheta_lon.df$names,
     x = labelData_sd_medianTheta_lon.df$x,
     y = labelData_sd_medianTheta_lon.df$y,
     col = labelData_sd_medianTheta_lon.df$col)
# geo group models
curve((GLMs_forPlotting[[4]][[2]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[2]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[4]][[2]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[2]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[4]][[2]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[2]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[4]][[2]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[4]][[2]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2

mean_f1

labelData_mean_f1_lat.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(65,40,40,32,32,65,36,50,33,50,33),
     y = c(1e-8,3e-8,-0.5e-8,0,-2.5e-8,-6e-8,-0.25e-8,-1.75e-8,-1.25e-8,-3.75e-8,2e-8),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato")
     )
View(labelData_mean_f1_lat.df)
pdf(file = "mean_f1_latBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$latwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$mean_f1, 
     xlim = c(min(allDivGeo.raw.df$latwgs84), max(allDivGeo.raw.df$latwgs84)),
     ylim = c(min(allDivGeo.raw.df$mean_f1, na.rm = T), max(allDivGeo.raw.df$mean_f1, na.rm = T)),
     col = "blue",
     xlab = "Latitude", ylab = "mean_d(theta)/dt", main = "mean_d(theta)/dt vs Lat")
# model for first species
curve((GLMs_forPlotting[[5]][[1]][[2]]$coefficients["latwgs84"])*x+
       GLMs_forPlotting[[5]][[1]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$mean_f1, 
       col = "green")
curve((GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$mean_f1, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$mean_f1, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$mean_f1, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$mean_f1, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$mean_f1, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[5]][[1]][[1]]$coefficients["y$latwgs84"])*x+
#        GLMs_forPlotting[[5]][[1]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$latwgs84), 
#       to = max(allDivGeo.raw.df$latwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_mean_f1_lat.df$names,
     x = labelData_mean_f1_lat.df$x,
     y = labelData_mean_f1_lat.df$y,
     col = labelData_mean_f1_lat.df$col)
# geo group models
curve((GLMs_forPlotting[[5]][[1]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[1]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[5]][[1]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[1]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[5]][[1]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[1]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[5]][[1]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[1]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2
labelData_mean_f1_lon.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(80,20,50,-5,5,65,-7,80,33,40,40),
     y = c(1e-8,3e-8,-0.75e-8,1e-8,-4e-8,-6e-8,-0.25e-8,-1.5e-8,1.25e-8,-2.75e-8,0.75e-8),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
View(labelData_mean_f1_lon.df)
pdf(file = "mean_f1_lonBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$lonwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$mean_f1, 
     xlim = c(min(allDivGeo.raw.df$lonwgs84), max(allDivGeo.raw.df$lonwgs84)),
     ylim = c(min(allDivGeo.raw.df$mean_f1, na.rm = T), max(allDivGeo.raw.df$mean_f1, na.rm = T)),
     col = "blue",
     xlab = "Longitude", ylab = "mean_d(theta)/dt", main = "mean_d(theta)/dt vs Lon")
# model for first species
curve((GLMs_forPlotting[[5]][[2]][[2]]$coefficients["lonwgs84"])*x+
       GLMs_forPlotting[[5]][[2]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$mean_f1, 
       col = "green")
curve((GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$mean_f1, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$mean_f1, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$mean_f1, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$mean_f1, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$mean_f1, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[5]][[2]][[1]]$coefficients["y$lonwgs84"])*x+
#        GLMs_forPlotting[[5]][[2]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$lonwgs84), 
#       to = max(allDivGeo.raw.df$lonwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_mean_f1_lon.df$names,
     x = labelData_mean_f1_lon.df$x,
     y = labelData_mean_f1_lon.df$y,
     col = labelData_mean_f1_lon.df$col)
# geo group models
curve((GLMs_forPlotting[[5]][[2]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[2]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[5]][[2]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[2]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[5]][[2]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[2]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[5]][[2]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[5]][[2]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2

sd_f1

labelData_sd_f1_lat.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(0,60,0,32,37,50,36,62,35,50,33),
     y = c(1e-8,5e-8,-0.5e-8,0,6.5e-8,8e-8,-0.25e-8,2.75e-8,3.5e-8,5e-8,2e-8),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato")
     )
View(labelData_sd_f1_lat.df)
pdf(file = "sd_f1_latBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$latwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$sd_f1, 
     xlim = c(min(allDivGeo.raw.df$latwgs84), max(allDivGeo.raw.df$latwgs84)),
     ylim = c(min(allDivGeo.raw.df$sd_f1, na.rm = T), max(allDivGeo.raw.df$sd_f1, na.rm = T)),
     col = "blue",
     xlab = "Latitude", ylab = "sd_d(theta)/dt", main = "sd_d(theta)/dt vs Lat")
# model for first species
curve((GLMs_forPlotting[[6]][[1]][[2]]$coefficients["latwgs84"])*x+
       GLMs_forPlotting[[6]][[1]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$sd_f1, 
       col = "green")
curve((GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$sd_f1, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$sd_f1, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$sd_f1, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$sd_f1, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$sd_f1, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[6]][[1]][[1]]$coefficients["y$latwgs84"])*x+
#        GLMs_forPlotting[[6]][[1]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$latwgs84), 
#       to = max(allDivGeo.raw.df$latwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_sd_f1_lat.df$names,
     x = labelData_sd_f1_lat.df$x,
     y = labelData_sd_f1_lat.df$y,
     col = labelData_sd_f1_lat.df$col)
# geo group models
curve((GLMs_forPlotting[[6]][[1]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[1]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[6]][[1]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[1]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[6]][[1]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[1]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[6]][[1]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[1]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2
labelData_sd_f1_lon.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(50,5,30,-10,-10,50,15,80,30,50,70),
     y = c(0,4.5e-8,1e-8,1e-8,4e-8,8e-8,-0.25e-8,2.75e-8,2e-8,4e-8,1.5e-8),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
View(labelData_sd_f1_lon.df)
pdf(file = "sd_f1_lonBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$lonwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$sd_f1, 
     xlim = c(min(allDivGeo.raw.df$lonwgs84), max(allDivGeo.raw.df$lonwgs84)),
     ylim = c(min(allDivGeo.raw.df$sd_f1, na.rm = T), max(allDivGeo.raw.df$sd_f1, na.rm = T)),
     col = "blue",
     xlab = "Longitude", ylab = "sd_d(theta)/dt", main = "sd_d(theta)/dt vs Lon")
# model for first species
curve((GLMs_forPlotting[[6]][[2]][[2]]$coefficients["lonwgs84"])*x+
       GLMs_forPlotting[[6]][[2]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$sd_f1, 
       col = "green")
curve((GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$sd_f1, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$sd_f1, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$sd_f1, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$sd_f1, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$sd_f1, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[6]][[2]][[1]]$coefficients["y$lonwgs84"])*x+
#        GLMs_forPlotting[[6]][[2]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$lonwgs84), 
#       to = max(allDivGeo.raw.df$lonwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_sd_f1_lon.df$names,
     x = labelData_sd_f1_lon.df$x,
     y = labelData_sd_f1_lon.df$y,
     col = labelData_sd_f1_lon.df$col)
# geo group models
curve((GLMs_forPlotting[[6]][[2]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[2]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[6]][[2]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[2]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[6]][[2]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[2]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[6]][[2]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[6]][[2]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2

mean_f2

labelData_mean_f2_lat.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(0,40,0,32,37,50,36,62,35,50,33),
     y = c(1e-8,-0.5e-10,-0.5e-8,0,6.5e-8,8e-8,-0.25e-8,2.75e-8,3.5e-8,5e-8,2e-8),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato")
     )
View(labelData_mean_f2_lat.df)
pdf(file = "mean_f2_latBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$latwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$mean_f2, 
     xlim = c(min(allDivGeo.raw.df$latwgs84), max(allDivGeo.raw.df$latwgs84)),
     ylim = c(min(allDivGeo.raw.df$mean_f2, na.rm = T), max(allDivGeo.raw.df$mean_f2, na.rm = T)),
     col = "blue",
     xlab = "Latitude", ylab = "mean_d2(theta)/d2t", main = "mean_d2(theta)/d2t vs Lat")
# model for first species
curve((GLMs_forPlotting[[7]][[1]][[2]]$coefficients["latwgs84"])*x+
       GLMs_forPlotting[[7]][[1]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$latwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$mean_f2, 
       col = "green")
curve((GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$latwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$mean_f2, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$latwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$mean_f2, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$latwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$mean_f2, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$latwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$mean_f2, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$latwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$latwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$mean_f2, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$latwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[7]][[1]][[1]]$coefficients["y$latwgs84"])*x+
#        GLMs_forPlotting[[7]][[1]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$latwgs84), 
#       to = max(allDivGeo.raw.df$latwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_mean_f2_lat.df$names,
     x = labelData_mean_f2_lat.df$x,
     y = labelData_mean_f2_lat.df$y,
     col = labelData_mean_f2_lat.df$col)
# geo group models
curve((GLMs_forPlotting[[7]][[1]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[1]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$latwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[7]][[1]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[1]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$latwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[7]][[1]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[1]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$latwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[7]][[1]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[1]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$latwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2
labelData_mean_f2_lon.df <- data.frame(
     names = c(levels(allDivGeo.raw.df$species),levels(allDivGeo.raw.df$geoGroups),levels(allDivGeo.raw.df$phyloGroups)[2:1]),
     x = c(100,0,30,-10,-10,50,15,80,30,50,70),
     y = c(0,-1e-10,1e-8,1e-8,4e-8,8e-8,-0.25e-8,2.75e-8,2e-8,4e-8,1.5e-8),
     col = c("blue", "green", "darkgreen", "orange", "purple", "red", "brown","navyblue","navyblue","tomato","tomato"))
View(labelData_mean_f2_lon.df)
pdf(file = "mean_f2_lonBYspeciesAndGeoGroup.pdf")
plot(x = subset(allDivGeo.raw.df, species == "BP")$lonwgs84,
     y = subset(allDivGeo.raw.df, species == "BP")$mean_f2, 
     xlim = c(min(allDivGeo.raw.df$lonwgs84), max(allDivGeo.raw.df$lonwgs84)),
     ylim = c(min(allDivGeo.raw.df$mean_f2, na.rm = T), max(allDivGeo.raw.df$mean_f2, na.rm = T)),
     col = "blue",
     xlab = "Longitude", ylab = "mean_d2(theta)/d2t", main = "mean_d2(theta)/d2t vs Lon")
# model for first species
curve((GLMs_forPlotting[[7]][[2]][[2]]$coefficients["lonwgs84"])*x+
       GLMs_forPlotting[[7]][[2]][[2]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "BP")$lonwgs84), 
      lty = "dashed", col = "blue", add = T)
# dots and modesl for all other species
points(x = subset(allDivGeo.raw.df, species == "FS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "FS")$mean_f2, 
       col = "green")
curve((GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesFS"])*x+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$speciesFS"], 
      from = min(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "FS")$lonwgs84), 
      lty = "dashed", col = "green", add = T)
points(x = subset(allDivGeo.raw.df, species == "PA")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PA")$mean_f2, 
       col = "darkgreen", pch = 0)
curve((GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPA"])*x+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$speciesPA"], 
      from = min(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PA")$lonwgs84), 
      lty = "dashed", col = "darkgreen", add = T)
points(x = subset(allDivGeo.raw.df, species == "PO")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PO")$mean_f2, 
       col = "orange", pch = 0)
curve((GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPO"])*x+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$speciesPO"], 
      from = min(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PO")$lonwgs84), 
      lty = "dashed", col = "orange", add = T)
points(x = subset(allDivGeo.raw.df, species == "PP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PP")$mean_f2, 
       col = "purple", pch = 2)
curve((GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPP"])*x+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$speciesPP"], 
      from = min(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PP")$lonwgs84), 
      lty = "dashed", col = "purple", add = T)
points(x = subset(allDivGeo.raw.df, species == "PS")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "PS")$mean_f2, 
       col = "red", pch = 2)
curve((GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesPS"])*x+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$speciesPS"], 
      from = min(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "PS")$lonwgs84), 
      lty = "dashed", col = "red", add = T)
points(x = subset(allDivGeo.raw.df, species == "QP")$lonwgs84,
       y = subset(allDivGeo.raw.df, species == "QP")$mean_f2, 
       col = "brown", pch = 0)
curve((GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84:y$speciesQP"])*x+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["(Intercept)"]+
       GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$speciesQP"], 
      from = min(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, species == "QP")$lonwgs84), 
      lty = "dashed", col = "brown", add = T)
## NOT USED
# overall mean
# curve((GLMs_forPlotting[[7]][[2]][[1]]$coefficients["y$lonwgs84"])*x+
#        GLMs_forPlotting[[7]][[2]][[1]]$coefficients["(Intercept)"], 
#       from = min(allDivGeo.raw.df$lonwgs84), 
#       to = max(allDivGeo.raw.df$lonwgs84), 
#       lwd = 2, col = "lightgrey", add = T)
text(labels = labelData_mean_f2_lon.df$names,
     x = labelData_mean_f2_lon.df$x,
     y = labelData_mean_f2_lon.df$y,
     col = labelData_mean_f2_lon.df$col)
# geo group models
curve((GLMs_forPlotting[[7]][[2]][[3]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[2]][[3]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "boreal")$lonwgs84), 
      col = "navyblue", add = T)
curve((GLMs_forPlotting[[7]][[2]][[4]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[2]][[4]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, geoGroups == "temperate")$lonwgs84), 
      col = "navyblue", lty = "dotted", add = T)
# phylogroup models
curve((GLMs_forPlotting[[7]][[2]][[5]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[2]][[5]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "gymno")$lonwgs84), 
      col = "tomato", add = T)
curve((GLMs_forPlotting[[7]][[2]][[6]]$coefficients[2])*x+
       GLMs_forPlotting[[7]][[2]][[6]]$coefficients["(Intercept)"], 
      from = min(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      to = max(subset(allDivGeo.raw.df, phyloGroups == "angio")$lonwgs84), 
      col = "tomato", lty = "dotted", add = T)
dev.off()
## quartz_off_screen 
##                 2

Is there a boreal vs temperate, angio vs gymno effect?

Notice that this also needs to be carried out on raw estimates, so as to be able to detect differences in mean between groups, in addition to differences in slope

# defining functions
geoBYgeoGroup_GLM <- function(x)
{
  tmp1 <- glm(x ~ allDivGeo.raw.df$latwgs84 * allDivGeo.raw.df$geoGroups)
  tmp2 <- glm(x ~ allDivGeo.raw.df$lonwgs84 * allDivGeo.raw.df$geoGroups)
  tmp3 <- TukeyHSD(x = aov(glm(x ~ allDivGeo.raw.df$latwgs84 * allDivGeo.raw.df$geoGroups)), which = "allDivGeo.raw.df$geoGroups", suppressWarnings = T)
  tmp4 <- TukeyHSD(x = aov(glm(x ~ allDivGeo.raw.df$lonwgs84 * allDivGeo.raw.df$geoGroups)), which = "allDivGeo.raw.df$geoGroups")
  return(list(tmp1,tmp2,tmp3,tmp4))
}
geoBYphyloGroup_GLM <- function(x)
{
  tmp1 <- glm(x ~ allDivGeo.raw.df$latwgs84 * allDivGeo.raw.df$phyloGroups)
  tmp2 <- glm(x ~ allDivGeo.raw.df$lonwgs84 * allDivGeo.raw.df$phyloGroups)
  tmp3 <- TukeyHSD(x = aov(glm(x ~ allDivGeo.raw.df$latwgs84 * allDivGeo.raw.df$phyloGroups)), which = "allDivGeo.raw.df$phyloGroups")
  tmp4 <- TukeyHSD(x = aov(glm(x ~ allDivGeo.raw.df$lonwgs84 * allDivGeo.raw.df$phyloGroups)), which = "allDivGeo.raw.df$phyloGroups")
  return(list(tmp1,tmp2,tmp3,tmp4))
}
#running functions
geoBYgeoGroup.glm <- apply(X = allDivGeo.raw.df[c(7,11,13,14,15,17,18)], MARGIN = 2, FUN = geoBYgeoGroup_GLM)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$geoGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$geoGroups
geoBYphyloGroup.glm <- apply(X = allDivGeo.raw.df[c(7,11,13,14,15,17,18)], MARGIN = 2, FUN = geoBYphyloGroup_GLM)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$latwgs84, allDivGeo.raw.df$phyloGroups
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## allDivGeo.raw.df$lonwgs84, allDivGeo.raw.df$phyloGroups

Extracting information

# threshold for alpha, Bonferroni-corrected
thr <- 0.05/28
# setting up matrix
geoBySpGroup.mt <- matrix(nrow = 7, ncol = 9)
colnames(geoBySpGroup.mt) <- c("divStat",
                                "LATgeoGroupDiff","LATgeoGroupInteraction",
                                "LONgeoGroupDiff","LONgeoGroupInteraction",
                                "LATphyloGroupDiff","LATphyloGroupInteraction",
                                "LONphyloGroupDiff","LONphyloGroupInteraction")
# collecting data, conditional on significance
for (i in 1:7)
{
   geoBySpGroup.mt[i,1] <- names(geoBYgeoGroup.glm)[i]
   if (geoBYgeoGroup.glm[[i]][[3]][[1]][4] < thr) geoBySpGroup.mt[i,2] <- -geoBYgeoGroup.glm[[i]][[3]][[1]][1]
   if (summary(geoBYgeoGroup.glm[[i]][[1]])$coefficients[4,4] < thr) geoBySpGroup.mt[i,3] <- -geoBYgeoGroup.glm[[i]][[1]]$effects[4]
   if (geoBYgeoGroup.glm[[i]][[4]][[1]][4] < thr) geoBySpGroup.mt[i,4] <- -geoBYgeoGroup.glm[[i]][[4]][[1]][1]
   if (summary(geoBYgeoGroup.glm[[i]][[2]])$coefficients[4,4] < thr) geoBySpGroup.mt[i,5] <- -geoBYgeoGroup.glm[[i]][[2]]$effects[4]
   if (geoBYphyloGroup.glm[[i]][[3]][[1]][4] < thr) geoBySpGroup.mt[i,6] <- geoBYphyloGroup.glm[[i]][[3]][[1]][1]
   if (summary(geoBYphyloGroup.glm[[i]][[1]])$coefficients[4,4] < thr) geoBySpGroup.mt[i,7] <- geoBYphyloGroup.glm[[i]][[1]]$effects[4]
   if (geoBYphyloGroup.glm[[i]][[4]][[1]][4] < thr) geoBySpGroup.mt[i,8] <- geoBYphyloGroup.glm[[i]][[4]][[1]][1]
   if (summary(geoBYphyloGroup.glm[[i]][[2]])$coefficients[4,4] < thr) geoBySpGroup.mt[i,9] <- geoBYphyloGroup.glm[[i]][[2]]$effects[4]
}
colnames(geoBySpGroup.mt)[c(2,4,6,8)] <- c("mean(boreal)-mean(temp)_LAT","mean(boreal)-mean(temp)_LON","mean(gymno)-mean(angio)_LAT","mean(gymno)-mean(angio)_LON")
colnames(geoBySpGroup.mt)[c(3,5,7,9)] <- c("slope(boreal)-slope(temp)_LAT","slope(boreal)-slope(temp)_LON","slope(gymno)-slope(angio)_LAT","slope(gymno)-slope(angio)_LON")
geoBySpGroup.mt[,2:9] <- signif(as.numeric(geoBySpGroup.mt[,2:9]),3)
write.table(x = geoBySpGroup.mt, file = "geoBySpGroup.txt", quote = F)